These pages document the python code for the sPyNNaker module which is part of the SpiNNaker Project.
This code depends on SpiNNUtils, SpiNNMachine, SpiNNStorageHandlers, SpiNNMan, PACMAN, DataSpecification, SpiNNFrontEndCommon (Combined_documentation).
sPyNNaker¶
Contents:
spynnaker package¶
Subpackages¶
spynnaker.pyNN package¶
Subpackages¶
spynnaker.pyNN.connections package¶
-
class
spynnaker.pyNN.connections.ethernet_control_connection.EthernetControlConnection(translator, local_host=None, local_port=None)[source]¶ Bases:
spinnman.connections.udp_packet_connections.eieio_connection.EIEIOConnectionA connection that can translate Ethernet control messages received from a Population
Parameters: - translator – The translator of multicast to control commands
- local_host – The optional host to listen on
- local_port – The optional port to listen on
-
class
spynnaker.pyNN.connections.spynnaker_live_spikes_connection.SpynnakerLiveSpikesConnection(receive_labels=None, send_labels=None, local_host=None, local_port=19999, live_packet_gather_label='LiveSpikeReceiver')[source]¶ Bases:
spinn_front_end_common.utilities.connections.live_event_connection.LiveEventConnectionA connection for receiving and sending live spikes from and to SpiNNaker
Parameters: - receive_labels (iterable of str) – Labels of population from which live spikes will be received.
- send_labels (iterable of str) – Labels of population to which live spikes will be sent
- local_host (str) – Optional specification of the local hostname or IP address of the interface to listen on
- local_port (int) – Optional specification of the local port to listen on. Must match the port that the toolchain will send the notification on (19999 by default)
-
send_spike(label, neuron_id, send_full_keys=False)[source]¶ Send a spike from a single neuron
Parameters: - label (str) – The label of the population from which the spike will originate
- neuron_id (int) – The ID of the neuron sending a spike
- send_full_keys (bool) – Determines whether to send full 32-bit keys, getting the key for each neuron from the database, or whether to send 16-bit neuron IDs directly
-
send_spikes(label, neuron_ids, send_full_keys=False)[source]¶ Send a number of spikes
Parameters: - label (str) – The label of the population from which the spikes will originate
- neuron_ids (list(int)) – array-like of neuron IDs sending spikes
- send_full_keys (bool) – Determines whether to send full 32-bit keys, getting the key for each neuron from the database, or whether to send 16-bit neuron IDs directly
spynnaker.pyNN.external_devices_models package¶
-
class
spynnaker.pyNN.external_devices_models.push_bot.push_bot_ethernet.push_bot_ethernet_device.PushBotEthernetDevice(protocol, device, uses_payload, time_between_send)[source]¶ -
An arbitrary PushBot device
Parameters: - protocol – The protocol instance to get commands from
- device – The Enum instance of the device to control
- uses_payload – True if the device uses a payload for control
-
device_control_key¶ The key that must be sent to the device to control it
Return type: int
-
device_control_max_value¶ The maximum value to send to the device
Return type: float
-
device_control_min_value¶ The minimum value to send to the device
Return type: float
-
device_control_partition_id¶ A partition ID to give to an outgoing edge partition that will control this device
Return type: str
-
device_control_timesteps_between_sending¶ The number of timesteps between sending commands to the device. This defines the “sampling interval” for the device.
Return type: int
-
device_control_uses_payload¶ True if the control of the device accepts an arbitrary valued payload, the value of which will change the devices behaviour
Return type: bool
-
protocol¶ The protocol instance, for use in the subclass
-
class
spynnaker.pyNN.external_devices_models.push_bot.push_bot_ethernet.push_bot_ethernet_laser_device.PushBotEthernetLaserDevice(laser, protocol, start_active_time=None, start_total_period=None, start_frequency=None, timesteps_between_send=None)[source]¶ Bases:
spynnaker.pyNN.external_devices_models.push_bot.push_bot_ethernet.push_bot_ethernet_device.PushBotEthernetDevice,spinn_front_end_common.abstract_models.abstract_send_me_multicast_commands_vertex.AbstractSendMeMulticastCommandsVertex,spinn_front_end_common.abstract_models.impl.provides_key_to_atom_mapping_impl.ProvidesKeyToAtomMappingImplThe Laser of a PushBot
Parameters: - laser – The PushBotLaser value to control
- protocol – The protocol instance to get commands from
- start_active_time – The “active time” value to send at the start
- start_total_period – The “total period” value to send at the start
- start_frequency – The “frequency” to send at the start
- timesteps_between_send – The number of timesteps between sending commands to the device, or None to use the default
-
pause_stop_commands¶ The commands needed when pausing or stopping simulation
Return type: iterable(MultiCastCommand)
-
set_command_protocol(command_protocol)[source]¶ Set the protocol use to send setup and shutdown commands, separately from the protocol used to control the device
-
start_resume_commands¶ The commands needed when starting or resuming simulation
Return type: iterable(MultiCastCommand)
-
timed_commands¶ The commands to be sent at given times in the simulation
Return type: iterable(MultiCastCommand)
-
class
spynnaker.pyNN.external_devices_models.push_bot.push_bot_ethernet.push_bot_ethernet_led_device.PushBotEthernetLEDDevice(led, protocol, start_active_time_front=None, start_active_time_back=None, start_total_period=None, start_frequency=None, timesteps_between_send=None)[source]¶ Bases:
spynnaker.pyNN.external_devices_models.push_bot.push_bot_ethernet.push_bot_ethernet_device.PushBotEthernetDevice,spinn_front_end_common.abstract_models.abstract_send_me_multicast_commands_vertex.AbstractSendMeMulticastCommandsVertex,spinn_front_end_common.abstract_models.impl.provides_key_to_atom_mapping_impl.ProvidesKeyToAtomMappingImplThe LED of a PushBot
Parameters: - led – The PushBotLED parameter to control
- protocol – The protocol instance to get commands from
- start_active_time_front – The “active time” to set for the front LED at the start
- start_active_time_back – The “active time” to set for the back LED at the start
- start_total_period – The “total period” to set at the start
- start_frequency – The “frequency” to set at the start
- timesteps_between_send – The number of timesteps between sending commands to the device, or None to use the default
-
pause_stop_commands¶ The commands needed when pausing or stopping simulation
Return type: iterable(MultiCastCommand)
-
set_command_protocol(command_protocol)[source]¶ Set the protocol use to send setup and shutdown commands, separately from the protocol used to control the device
-
start_resume_commands¶ The commands needed when starting or resuming simulation
Return type: iterable(MultiCastCommand)
-
timed_commands¶ The commands to be sent at given times in the simulation
Return type: iterable(MultiCastCommand)
-
class
spynnaker.pyNN.external_devices_models.push_bot.push_bot_ethernet.push_bot_ethernet_motor_device.PushBotEthernetMotorDevice(motor, protocol, timesteps_between_send=None)[source]¶ Bases:
spynnaker.pyNN.external_devices_models.push_bot.push_bot_ethernet.push_bot_ethernet_device.PushBotEthernetDevice,spinn_front_end_common.abstract_models.abstract_send_me_multicast_commands_vertex.AbstractSendMeMulticastCommandsVertex,spinn_front_end_common.abstract_models.impl.provides_key_to_atom_mapping_impl.ProvidesKeyToAtomMappingImplThe motor of a PushBot
Parameters: - motor – a PushBotMotor value to indicate the motor to control
- protocol – The protocol used to control the device
- timesteps_between_send – The number of timesteps between sending commands to the device, or None to use the default
-
pause_stop_commands¶ The commands needed when pausing or stopping simulation
Return type: iterable(MultiCastCommand)
-
set_command_protocol(command_protocol)[source]¶ Set the protocol use to send setup and shutdown commands, separately from the protocol used to control the device
-
start_resume_commands¶ The commands needed when starting or resuming simulation
Return type: iterable(MultiCastCommand)
-
timed_commands¶ The commands to be sent at given times in the simulation
Return type: iterable(MultiCastCommand)
-
class
spynnaker.pyNN.external_devices_models.push_bot.push_bot_ethernet.push_bot_translator.PushBotTranslator(protocol, pushbot_wifi_connection)[source]¶ Bases:
spynnaker.pyNN.external_devices_models.abstract_ethernet_translator.AbstractEthernetTranslatorTranslates packets between PushBot Multicast packets and PushBot Wi-Fi Commands
Parameters: - protocol – The instance of the PushBot protocol to get keys from
- pushbot_wifi_connection – A Wi-Fi connection to the PushBot
-
class
spynnaker.pyNN.external_devices_models.push_bot.push_bot_ethernet.push_bot_wifi_connection.PushBotWIFIConnection(remote_host, remote_port=56000)[source]¶ Bases:
spinnman.connections.abstract_classes.connection.Connection,spinnman.connections.abstract_classes.listenable.ListenableA connection to a PushBot via Wi-Fi.
Parameters: - remote_host (str) – The IP address of the PushBot
- remote_port (int) – The port number of the PushBot (default 56000)
Raises: spinnman.exceptions.SpinnmanIOException – If there is an error setting up the communication channel
-
is_ready_to_receive(timeout=0)[source]¶ Determines if there is an SCP packet to be read without blocking
Parameters: timeout (int) – The time to wait before returning if the connection is not ready Returns: True if there is an SCP packet to be read Return type: bool
-
local_ip_address¶ The local IP address to which the connection is bound.
Returns: The local IP address as a dotted string, e.g. 0.0.0.0 Return type: str Raises: None – No known exceptions are thrown
-
local_port¶ The local port to which the connection is bound.
Returns: The local port number Return type: int Raises: None – No known exceptions are thrown
-
receive(timeout=None)[source]¶ Receive data from the connection
Parameters: timeout (float or None) – The timeout, or None to wait forever
Returns: The data received
Return type: bytestring
Raises: - SpinnmanTimeoutException – If a timeout occurs before any data is received
- SpinnmanIOException – If an error occurs receiving the data
-
remote_ip_address¶ The remote IP address to which the connection is connected.
Returns: The remote IP address as a dotted string, or None if not connected remotely Return type: str
-
remote_port¶ The remote port to which the connection is connected.
Returns: The remote port, or None if not connected remotely Return type: int
-
spynnaker.pyNN.external_devices_models.push_bot.push_bot_ethernet.push_bot_wifi_connection.get_pushbot_wifi_connection(remote_host, remote_port=56000)[source]¶ Get an existing connection to a PushBot, or make a new one.
Parameters: - remote_host (str) – The IP address of the PushBot
- remote_port (int) – The port number of the PushBot (default 56000)
-
class
spynnaker.pyNN.external_devices_models.push_bot.push_bot_parameters.push_bot_retina_resolution.PushBotRetinaResolution[source]¶ Bases:
enum.EnumAn enumeration.
-
DOWNSAMPLE_16_X_16= <RetinaKey.DOWNSAMPLE_16_X_16: 268435456>¶
-
DOWNSAMPLE_32_X_32= <RetinaKey.DOWNSAMPLE_32_X_32: 201326592>¶
-
DOWNSAMPLE_64_X_64= <RetinaKey.DOWNSAMPLE_64_X_64: 134217728>¶
-
NATIVE_128_X_128= <RetinaKey.NATIVE_128_X_128: 67108864>¶
-
-
class
spynnaker.pyNN.external_devices_models.push_bot.push_bot_parameters.push_bot_retina_viewer.PushBotRetinaViewer(resolution, port=0, display_max=33.0, frame_time_ms=10, decay_time_constant_ms=100)[source]¶ Bases:
threading.Thread-
local_host¶
-
local_port¶
-
run()[source]¶ Method representing the thread’s activity.
You may override this method in a subclass. The standard run() method invokes the callable object passed to the object’s constructor as the target argument, if any, with sequential and keyword arguments taken from the args and kwargs arguments, respectively.
-
-
class
spynnaker.pyNN.external_devices_models.push_bot.push_bot_parameters.PushBotLaser[source]¶ -
An enumeration.
-
LASER_ACTIVE_TIME= 1¶
-
LASER_FREQUENCY= 2¶
-
LASER_TOTAL_PERIOD= 0¶
-
-
class
spynnaker.pyNN.external_devices_models.push_bot.push_bot_parameters.PushBotLED[source]¶ -
An enumeration.
-
LED_BACK_ACTIVE_TIME= 2¶
-
LED_FREQUENCY= 3¶
-
LED_FRONT_ACTIVE_TIME= 1¶
-
LED_TOTAL_PERIOD= 0¶
-
-
class
spynnaker.pyNN.external_devices_models.push_bot.push_bot_parameters.PushBotMotor[source]¶ -
An enumeration.
-
MOTOR_0_LEAKY= 1¶
-
MOTOR_0_PERMANENT= 0¶
-
MOTOR_1_LEAKY= 3¶
-
MOTOR_1_PERMANENT= 2¶
-
-
class
spynnaker.pyNN.external_devices_models.push_bot.push_bot_parameters.PushBotSpeaker[source]¶ -
An enumeration.
-
SPEAKER_ACTIVE_TIME= 1¶
-
SPEAKER_MELODY= 3¶
-
SPEAKER_TONE= 2¶
-
SPEAKER_TOTAL_PERIOD= 0¶
-
-
class
spynnaker.pyNN.external_devices_models.push_bot.push_bot_parameters.PushBotRetinaResolution[source]¶ Bases:
enum.EnumAn enumeration.
-
DOWNSAMPLE_16_X_16= <RetinaKey.DOWNSAMPLE_16_X_16: 268435456>¶
-
DOWNSAMPLE_32_X_32= <RetinaKey.DOWNSAMPLE_32_X_32: 201326592>¶
-
DOWNSAMPLE_64_X_64= <RetinaKey.DOWNSAMPLE_64_X_64: 134217728>¶
-
NATIVE_128_X_128= <RetinaKey.NATIVE_128_X_128: 67108864>¶
-
-
class
spynnaker.pyNN.external_devices_models.push_bot.push_bot_parameters.PushBotRetinaViewer(resolution, port=0, display_max=33.0, frame_time_ms=10, decay_time_constant_ms=100)[source]¶ Bases:
threading.Thread-
local_host¶
-
local_port¶
-
run()[source]¶ Method representing the thread’s activity.
You may override this method in a subclass. The standard run() method invokes the callable object passed to the object’s constructor as the target argument, if any, with sequential and keyword arguments taken from the args and kwargs arguments, respectively.
-
-
class
spynnaker.pyNN.external_devices_models.push_bot.abstract_push_bot_retina_device.AbstractPushBotRetinaDevice(protocol, resolution)[source]¶ Bases:
spinn_front_end_common.abstract_models.abstract_send_me_multicast_commands_vertex.AbstractSendMeMulticastCommandsVertex,spinn_front_end_common.abstract_models.impl.provides_key_to_atom_mapping_impl.ProvidesKeyToAtomMappingImpl-
pause_stop_commands¶ The commands needed when pausing or stopping simulation
Return type: iterable(MultiCastCommand)
-
start_resume_commands¶ The commands needed when starting or resuming simulation
Return type: iterable(MultiCastCommand)
-
timed_commands¶ The commands to be sent at given times in the simulation
Return type: iterable(MultiCastCommand)
-
-
class
spynnaker.pyNN.external_devices_models.push_bot.AbstractPushBotOutputDevice[source]¶ Bases:
enum.EnumAn enumeration.
-
max_value¶
-
min_value¶
-
protocol_property¶
-
send_type¶
-
time_between_send¶
-
-
class
spynnaker.pyNN.external_devices_models.push_bot.AbstractPushBotRetinaDevice(protocol, resolution)[source]¶ Bases:
spinn_front_end_common.abstract_models.abstract_send_me_multicast_commands_vertex.AbstractSendMeMulticastCommandsVertex,spinn_front_end_common.abstract_models.impl.provides_key_to_atom_mapping_impl.ProvidesKeyToAtomMappingImpl-
pause_stop_commands¶ The commands needed when pausing or stopping simulation
Return type: iterable(MultiCastCommand)
-
start_resume_commands¶ The commands needed when starting or resuming simulation
Return type: iterable(MultiCastCommand)
-
timed_commands¶ The commands to be sent at given times in the simulation
Return type: iterable(MultiCastCommand)
-
-
class
spynnaker.pyNN.external_devices_models.abstract_ethernet_controller.AbstractEthernetController[source]¶ Bases:
objectA controller that can send multicast packets which can be received over Ethernet and translated to control an external device
-
get_external_devices()[source]¶ Get the external devices that are to be controlled by the controller
-
get_message_translator()[source]¶ Get the translator of messages
Return type: spynnaker.pyNN.external_devices_models.AbstractEthernetTranslator
-
-
class
spynnaker.pyNN.external_devices_models.abstract_multicast_controllable_device.AbstractMulticastControllableDevice[source]¶ Bases:
objectA device that can be controlled by sending multicast packets to it, either directly, or via Ethernet using an AbstractEthernetTranslator
-
device_control_key¶ The key that must be sent to the device to control it
Return type: int
-
device_control_max_value¶ The maximum value to send to the device
Return type: float
-
device_control_min_value¶ The minimum value to send to the device
Return type: float
-
device_control_partition_id¶ A partition ID to give to an outgoing edge partition that will control this device
Return type: str
-
device_control_scaling_factor¶ The scaling factor used to send the payload to this device.
Return type: int
-
device_control_timesteps_between_sending¶ The number of timesteps between sending commands to the device. This defines the “sampling interval” for the device.
Return type: int
-
device_control_uses_payload¶ True if the control of the device accepts an arbitrary valued payload, the value of which will change the devices behaviour
Return type: bool
-
-
class
spynnaker.pyNN.external_devices_models.abstract_multicast_controllable_device.SendType[source]¶ Bases:
enum.EnumThe data type to be sent in the payload of the multicast packet
-
SEND_TYPE_ACCUM= 2¶
-
SEND_TYPE_FRACT= 4¶
-
SEND_TYPE_INT= 0¶
-
SEND_TYPE_UACCUM= 3¶
-
SEND_TYPE_UFRACT= 5¶
-
SEND_TYPE_UINT= 1¶
-
-
class
spynnaker.pyNN.external_devices_models.arbitrary_fpga_device.ArbitraryFPGADevice(n_neurons, fpga_link_id, fpga_id, board_address=None, label=None)[source]¶ Bases:
pacman.model.graphs.application.application_fpga_vertex.ApplicationFPGAVertex,spinn_front_end_common.abstract_models.impl.provides_key_to_atom_mapping_impl.ProvidesKeyToAtomMappingImpl
-
class
spynnaker.pyNN.external_devices_models.external_device_lif_control.ExternalDeviceLifControl(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandardAbstract control module for the PushBot, based on the LIF neuron, but without spikes, and using the voltage as the output to the various devices
-
create_vertex(n_neurons, label, constraints, spikes_per_second, ring_buffer_sigma, incoming_spike_buffer_size)[source]¶ Create a vertex for a population of the model
Parameters: - n_neurons (int) – The number of neurons in the population
- label (str) – The label to give to the vertex
- constraints (list or None) – A list of constraints to give to the vertex, or None
Returns: An application vertex for the population
Return type:
-
-
class
spynnaker.pyNN.external_devices_models.external_device_lif_control_vertex.ExternalDeviceLifControlVertex(devices, create_edges, max_atoms_per_core, neuron_impl, pynn_model, translator=None, spikes_per_second=None, label=None, ring_buffer_sigma=None, incoming_spike_buffer_size=None, constraints=None)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_population_vertex.AbstractPopulationVertex,spynnaker.pyNN.external_devices_models.abstract_ethernet_controller.AbstractEthernetController,spinn_front_end_common.abstract_models.abstract_provides_outgoing_partition_constraints.AbstractProvidesOutgoingPartitionConstraints,spinn_front_end_common.abstract_models.abstract_vertex_with_dependent_vertices.AbstractVertexWithEdgeToDependentVerticesAbstract control module for the pushbot, based on the LIF neuron, but without spikes, and using the voltage as the output to the various devices
Parameters: - n_neurons – The number of neurons in the population
- devices – The AbstractMulticastControllableDevice instances to be controlled by the population
- create_edges – True if edges to the devices should be added by this dev (set to False if using the dev over Ethernet using a translator)
- translator – Translator to be used when used for Ethernet communication. Must be provided if the dev is to be controlled over Ethernet.
-
dependent_vertices()[source]¶ Return the vertices which this vertex depends upon
Return type: iterable(ApplicationVertex)
-
edge_partition_identifiers_for_dependent_vertex(vertex)[source]¶ Return the dependent edge identifiers for a particular dependent vertex.
Parameters: vertex (ApplicationVertex) – Return type: iterable(str)
-
get_external_devices()[source]¶ Get the external devices that are to be controlled by the controller
-
get_message_translator()[source]¶ Get the translator of messages
Return type: spynnaker.pyNN.external_devices_models.AbstractEthernetTranslator
-
get_outgoing_partition_constraints(partition)[source]¶ Get constraints to be added to the given edge that comes out of this vertex.
Parameters: partition (AbstractOutgoingEdgePartition) – An edge that comes out of this vertex Returns: A list of constraints Return type: list(AbstractConstraint)
-
get_outgoing_partition_ids()[source]¶ Get the partition IDs of messages coming out of the controller
Return type: list(str)
-
routing_key_partition_atom_mapping(routing_info, partition)[source]¶ Returns a list of atom to key mapping.
Parameters: - routing_info (RoutingInfo) – the routing info object to consider
- partition (AbstractOutgoingEdgePartition) – the routing partition to handle.
Returns: a iterable of tuples of atom IDs to keys.
Return type: iterable(tuple(int,int))
-
class
spynnaker.pyNN.external_devices_models.external_spinnaker_link_cochlea_device.ExternalCochleaDevice(n_neurons, spinnaker_link, label=None, board_address=None)[source]¶ Bases:
pacman.model.graphs.application.application_spinnaker_link_vertex.ApplicationSpiNNakerLinkVertex,spinn_front_end_common.abstract_models.impl.provides_key_to_atom_mapping_impl.ProvidesKeyToAtomMappingImpl
-
class
spynnaker.pyNN.external_devices_models.external_spinnaker_link_fpga_retina_device.ExternalFPGARetinaDevice(mode, retina_key, spinnaker_link_id, polarity, label=None, board_address=None)[source]¶ Bases:
pacman.model.graphs.application.application_spinnaker_link_vertex.ApplicationSpiNNakerLinkVertex,spinn_front_end_common.abstract_models.abstract_send_me_multicast_commands_vertex.AbstractSendMeMulticastCommandsVertex,spinn_front_end_common.abstract_models.abstract_provides_outgoing_partition_constraints.AbstractProvidesOutgoingPartitionConstraints,spinn_front_end_common.abstract_models.impl.provides_key_to_atom_mapping_impl.ProvidesKeyToAtomMappingImplParameters: - mode – The retina “mode”
- retina_key – The value of the top 16-bits of the key
- spinnaker_link_id – The SpiNNaker link to which the retina is connected
- polarity – The “polarity” of the retina data
- label –
- board_address –
-
DOWN_POLARITY= 'DOWN'¶
-
MERGED_POLARITY= 'MERGED'¶
-
MODE_128= '128'¶
-
MODE_16= '16'¶
-
MODE_32= '32'¶
-
MODE_64= '64'¶
-
UP_POLARITY= 'UP'¶
-
get_outgoing_partition_constraints(partition)[source]¶ Get constraints to be added to the given edge that comes out of this vertex.
Parameters: partition (AbstractOutgoingEdgePartition) – An edge that comes out of this vertex Returns: A list of constraints Return type: list(AbstractConstraint)
-
pause_stop_commands¶ The commands needed when pausing or stopping simulation
Return type: iterable(MultiCastCommand)
-
start_resume_commands¶ The commands needed when starting or resuming simulation
Return type: iterable(MultiCastCommand)
-
timed_commands¶ The commands to be sent at given times in the simulation
Return type: iterable(MultiCastCommand)
-
spynnaker.pyNN.external_devices_models.external_spinnaker_link_fpga_retina_device.get_spike_value_from_fpga_retina(key, mode)[source]¶
-
class
spynnaker.pyNN.external_devices_models.munich_spinnaker_link_motor_device.MunichMotorDevice(spinnaker_link_id, board_address=None, speed=30, sample_time=4096, update_time=512, delay_time=5, delta_threshold=23, continue_if_not_different=True, label=None)[source]¶ Bases:
pacman.model.graphs.application.application_vertex.ApplicationVertex,spinn_front_end_common.abstract_models.abstract_vertex_with_dependent_vertices.AbstractVertexWithEdgeToDependentVertices,spinn_front_end_common.abstract_models.abstract_generates_data_specification.AbstractGeneratesDataSpecification,spinn_front_end_common.abstract_models.abstract_has_associated_binary.AbstractHasAssociatedBinary,spinn_front_end_common.abstract_models.abstract_provides_outgoing_partition_constraints.AbstractProvidesOutgoingPartitionConstraints,spinn_front_end_common.abstract_models.impl.provides_key_to_atom_mapping_impl.ProvidesKeyToAtomMappingImplAn Omnibot motor control device - has a real vertex and an external device vertex
-
PARAMS_REGION= 1¶
-
PARAMS_SIZE= 28¶
-
SYSTEM_REGION= 0¶
-
create_machine_vertex(vertex_slice, resources_required, label=None, constraints=None)[source]¶ Create a machine vertex from this application vertex
Parameters: - vertex_slice (Slice) – The slice of atoms that the machine vertex will cover
- resources_required (ResourceContainer) – the resources used by the machine vertex
- label (str or None) – human readable label for the machine vertex
- constraints (iterable(AbstractConstraint)) – Constraints to be passed on to the machine vertex
-
default_initial_values= {}¶
-
default_parameters= {'board_address': None, 'continue_if_not_different': True, 'delay_time': 5, 'delta_threshold': 23, 'label': None, 'sample_time': 4096, 'speed': 30, 'update_time': 512}¶
-
dependent_vertices()[source]¶ Return the vertices which this vertex depends upon
Return type: iterable(ApplicationVertex) Return the vertices which this vertex depends upon
-
edge_partition_identifiers_for_dependent_vertex(vertex)[source]¶ Return the dependent edge identifiers for a particular dependent vertex.
Parameters: vertex (ApplicationVertex) – Return type: iterable(str) Return the dependent edge identifier
-
generate_data_specification(spec, placement, routing_info, machine_time_step, time_scale_factor)[source]¶ Generate a data specification.
Parameters: - spec (DataSpecificationGenerator) – The data specification to write to
- placement (Placement) – the placement the vertex is located at
Return type: None
-
get_binary_start_type()[source]¶ Get the start type of the binary to be run.
Return type: ExecutableType
-
get_outgoing_partition_constraints(partition)[source]¶ Get constraints to be added to the given edge that comes out of this vertex.
Parameters: partition (AbstractOutgoingEdgePartition) – An edge that comes out of this vertex Returns: A list of constraints Return type: list(AbstractConstraint)
-
get_resources_used_by_atoms(vertex_slice)[source]¶ Get the separate resource requirements for a range of atoms
Parameters: vertex_slice (Slice) – the low value of atoms to calculate resources from Returns: a Resource container that contains a CPUCyclesPerTickResource, DTCMResource and SDRAMResource Return type: ResourceContainer Raises: None – this method does not raise any known exception
-
n_atoms¶ The number of atoms in the vertex
Return type: int
-
-
class
spynnaker.pyNN.external_devices_models.munich_spinnaker_link_retina_device.MunichRetinaDevice(retina_key, spinnaker_link_id, position, label=None, polarity=None, board_address=None)[source]¶ Bases:
pacman.model.graphs.application.application_spinnaker_link_vertex.ApplicationSpiNNakerLinkVertex,spinn_front_end_common.abstract_models.abstract_send_me_multicast_commands_vertex.AbstractSendMeMulticastCommandsVertex,spinn_front_end_common.abstract_models.abstract_provides_outgoing_partition_constraints.AbstractProvidesOutgoingPartitionConstraints,spinn_front_end_common.abstract_models.impl.provides_key_to_atom_mapping_impl.ProvidesKeyToAtomMappingImpl-
DOWN_POLARITY= 'DOWN'¶
-
LEFT_RETINA= 'LEFT'¶
-
LEFT_RETINA_DISABLE= 69¶
-
LEFT_RETINA_ENABLE= 69¶
-
LEFT_RETINA_KEY_SET= 67¶
-
MANAGEMENT_BIT= 1024¶
-
MANAGEMENT_MASK= 4294965248¶
-
MERGED_POLARITY= 'MERGED'¶
-
RIGHT_RETINA= 'RIGHT'¶
-
RIGHT_RETINA_DISABLE= 70¶
-
RIGHT_RETINA_ENABLE= 70¶
-
RIGHT_RETINA_KEY_SET= 68¶
-
UP_POLARITY= 'UP'¶
-
default_parameters= {'board_address': None, 'label': 'MunichRetinaDevice', 'polarity': None}¶
-
get_outgoing_partition_constraints(partition)[source]¶ Get constraints to be added to the given edge that comes out of this vertex.
Parameters: partition (AbstractOutgoingEdgePartition) – An edge that comes out of this vertex Returns: A list of constraints Return type: list(AbstractConstraint)
-
pause_stop_commands¶ The commands needed when pausing or stopping simulation
Return type: iterable(MultiCastCommand)
-
start_resume_commands¶ The commands needed when starting or resuming simulation
Return type: iterable(MultiCastCommand)
-
timed_commands¶ The commands to be sent at given times in the simulation
Return type: iterable(MultiCastCommand)
-
-
spynnaker.pyNN.external_devices_models.munich_spinnaker_link_retina_device.get_spike_value_from_robot_retina(key)[source]¶
-
class
spynnaker.pyNN.external_devices_models.threshold_type_multicast_device_control.ThresholdTypeMulticastDeviceControl(device)[source]¶ Bases:
spynnaker.pyNN.models.neuron.threshold_types.abstract_threshold_type.AbstractThresholdTypeA threshold type that can send multicast keys with the value of membrane voltage as the payload
-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
get_values(parameters, state_variables, vertex_slice)[source]¶ Get the values to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of variables being retrieved
Returns: A list with the same length as self.struct.field_types
Return type: A list of (single value or list of values or RangedList)
- parameters (
-
has_variable(variable)[source]¶ Determine if this component has a variable by the given name
Parameters: variable (str) – The name of the variable Return type: bool
-
update_values(values, parameters, state_variables)[source]¶ Update the parameters and state variables with the given struct values that have been read from the machine
Parameters: - values – The values read from the machine, one for each struct element
- parameters – The holder of the parameters to update
- state_variables – The holder of the state variables to update
-
-
class
spynnaker.pyNN.external_devices_models.AbstractEthernetController[source]¶ Bases:
objectA controller that can send multicast packets which can be received over Ethernet and translated to control an external device
-
get_external_devices()[source]¶ Get the external devices that are to be controlled by the controller
-
get_message_translator()[source]¶ Get the translator of messages
Return type: spynnaker.pyNN.external_devices_models.AbstractEthernetTranslator
-
-
class
spynnaker.pyNN.external_devices_models.AbstractEthernetSensor[source]¶ Bases:
object
-
class
spynnaker.pyNN.external_devices_models.AbstractEthernetTranslator[source]¶ Bases:
objectA module that can translate packets received over Ethernet into control of an external device
-
class
spynnaker.pyNN.external_devices_models.ArbitraryFPGADevice(n_neurons, fpga_link_id, fpga_id, board_address=None, label=None)[source]¶ Bases:
pacman.model.graphs.application.application_fpga_vertex.ApplicationFPGAVertex,spinn_front_end_common.abstract_models.impl.provides_key_to_atom_mapping_impl.ProvidesKeyToAtomMappingImpl
-
class
spynnaker.pyNN.external_devices_models.AbstractMulticastControllableDevice[source]¶ Bases:
objectA device that can be controlled by sending multicast packets to it, either directly, or via Ethernet using an AbstractEthernetTranslator
-
device_control_key¶ The key that must be sent to the device to control it
Return type: int
-
device_control_max_value¶ The maximum value to send to the device
Return type: float
-
device_control_min_value¶ The minimum value to send to the device
Return type: float
-
device_control_partition_id¶ A partition ID to give to an outgoing edge partition that will control this device
Return type: str
-
device_control_scaling_factor¶ The scaling factor used to send the payload to this device.
Return type: int
-
device_control_timesteps_between_sending¶ The number of timesteps between sending commands to the device. This defines the “sampling interval” for the device.
Return type: int
-
device_control_uses_payload¶ True if the control of the device accepts an arbitrary valued payload, the value of which will change the devices behaviour
Return type: bool
-
-
class
spynnaker.pyNN.external_devices_models.ExternalDeviceLifControl(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandardAbstract control module for the PushBot, based on the LIF neuron, but without spikes, and using the voltage as the output to the various devices
-
create_vertex(n_neurons, label, constraints, spikes_per_second, ring_buffer_sigma, incoming_spike_buffer_size)[source]¶ Create a vertex for a population of the model
Parameters: - n_neurons (int) – The number of neurons in the population
- label (str) – The label to give to the vertex
- constraints (list or None) – A list of constraints to give to the vertex, or None
Returns: An application vertex for the population
Return type:
-
-
class
spynnaker.pyNN.external_devices_models.ExternalCochleaDevice(n_neurons, spinnaker_link, label=None, board_address=None)[source]¶ Bases:
pacman.model.graphs.application.application_spinnaker_link_vertex.ApplicationSpiNNakerLinkVertex,spinn_front_end_common.abstract_models.impl.provides_key_to_atom_mapping_impl.ProvidesKeyToAtomMappingImpl
-
class
spynnaker.pyNN.external_devices_models.ExternalFPGARetinaDevice(mode, retina_key, spinnaker_link_id, polarity, label=None, board_address=None)[source]¶ Bases:
pacman.model.graphs.application.application_spinnaker_link_vertex.ApplicationSpiNNakerLinkVertex,spinn_front_end_common.abstract_models.abstract_send_me_multicast_commands_vertex.AbstractSendMeMulticastCommandsVertex,spinn_front_end_common.abstract_models.abstract_provides_outgoing_partition_constraints.AbstractProvidesOutgoingPartitionConstraints,spinn_front_end_common.abstract_models.impl.provides_key_to_atom_mapping_impl.ProvidesKeyToAtomMappingImplParameters: - mode – The retina “mode”
- retina_key – The value of the top 16-bits of the key
- spinnaker_link_id – The SpiNNaker link to which the retina is connected
- polarity – The “polarity” of the retina data
- label –
- board_address –
-
DOWN_POLARITY= 'DOWN'¶
-
MERGED_POLARITY= 'MERGED'¶
-
MODE_128= '128'¶
-
MODE_16= '16'¶
-
MODE_32= '32'¶
-
MODE_64= '64'¶
-
UP_POLARITY= 'UP'¶
-
get_outgoing_partition_constraints(partition)[source]¶ Get constraints to be added to the given edge that comes out of this vertex.
Parameters: partition (AbstractOutgoingEdgePartition) – An edge that comes out of this vertex Returns: A list of constraints Return type: list(AbstractConstraint)
-
pause_stop_commands¶ The commands needed when pausing or stopping simulation
Return type: iterable(MultiCastCommand)
-
start_resume_commands¶ The commands needed when starting or resuming simulation
Return type: iterable(MultiCastCommand)
-
timed_commands¶ The commands to be sent at given times in the simulation
Return type: iterable(MultiCastCommand)
-
class
spynnaker.pyNN.external_devices_models.MunichMotorDevice(spinnaker_link_id, board_address=None, speed=30, sample_time=4096, update_time=512, delay_time=5, delta_threshold=23, continue_if_not_different=True, label=None)[source]¶ Bases:
pacman.model.graphs.application.application_vertex.ApplicationVertex,spinn_front_end_common.abstract_models.abstract_vertex_with_dependent_vertices.AbstractVertexWithEdgeToDependentVertices,spinn_front_end_common.abstract_models.abstract_generates_data_specification.AbstractGeneratesDataSpecification,spinn_front_end_common.abstract_models.abstract_has_associated_binary.AbstractHasAssociatedBinary,spinn_front_end_common.abstract_models.abstract_provides_outgoing_partition_constraints.AbstractProvidesOutgoingPartitionConstraints,spinn_front_end_common.abstract_models.impl.provides_key_to_atom_mapping_impl.ProvidesKeyToAtomMappingImplAn Omnibot motor control device - has a real vertex and an external device vertex
-
PARAMS_REGION= 1¶
-
PARAMS_SIZE= 28¶
-
SYSTEM_REGION= 0¶
-
create_machine_vertex(vertex_slice, resources_required, label=None, constraints=None)[source]¶ Create a machine vertex from this application vertex
Parameters: - vertex_slice (Slice) – The slice of atoms that the machine vertex will cover
- resources_required (ResourceContainer) – the resources used by the machine vertex
- label (str or None) – human readable label for the machine vertex
- constraints (iterable(AbstractConstraint)) – Constraints to be passed on to the machine vertex
-
default_initial_values= {}¶
-
default_parameters= {'board_address': None, 'continue_if_not_different': True, 'delay_time': 5, 'delta_threshold': 23, 'label': None, 'sample_time': 4096, 'speed': 30, 'update_time': 512}¶
-
dependent_vertices()[source]¶ Return the vertices which this vertex depends upon
Return type: iterable(ApplicationVertex) Return the vertices which this vertex depends upon
-
edge_partition_identifiers_for_dependent_vertex(vertex)[source]¶ Return the dependent edge identifiers for a particular dependent vertex.
Parameters: vertex (ApplicationVertex) – Return type: iterable(str) Return the dependent edge identifier
-
generate_data_specification(spec, placement, routing_info, machine_time_step, time_scale_factor)[source]¶ Generate a data specification.
Parameters: - spec (DataSpecificationGenerator) – The data specification to write to
- placement (Placement) – the placement the vertex is located at
Return type: None
-
get_binary_start_type()[source]¶ Get the start type of the binary to be run.
Return type: ExecutableType
-
get_outgoing_partition_constraints(partition)[source]¶ Get constraints to be added to the given edge that comes out of this vertex.
Parameters: partition (AbstractOutgoingEdgePartition) – An edge that comes out of this vertex Returns: A list of constraints Return type: list(AbstractConstraint)
-
get_resources_used_by_atoms(vertex_slice)[source]¶ Get the separate resource requirements for a range of atoms
Parameters: vertex_slice (Slice) – the low value of atoms to calculate resources from Returns: a Resource container that contains a CPUCyclesPerTickResource, DTCMResource and SDRAMResource Return type: ResourceContainer Raises: None – this method does not raise any known exception
-
n_atoms¶ The number of atoms in the vertex
Return type: int
-
-
class
spynnaker.pyNN.external_devices_models.MunichRetinaDevice(retina_key, spinnaker_link_id, position, label=None, polarity=None, board_address=None)[source]¶ Bases:
pacman.model.graphs.application.application_spinnaker_link_vertex.ApplicationSpiNNakerLinkVertex,spinn_front_end_common.abstract_models.abstract_send_me_multicast_commands_vertex.AbstractSendMeMulticastCommandsVertex,spinn_front_end_common.abstract_models.abstract_provides_outgoing_partition_constraints.AbstractProvidesOutgoingPartitionConstraints,spinn_front_end_common.abstract_models.impl.provides_key_to_atom_mapping_impl.ProvidesKeyToAtomMappingImpl-
DOWN_POLARITY= 'DOWN'¶
-
LEFT_RETINA= 'LEFT'¶
-
LEFT_RETINA_DISABLE= 69¶
-
LEFT_RETINA_ENABLE= 69¶
-
LEFT_RETINA_KEY_SET= 67¶
-
MANAGEMENT_BIT= 1024¶
-
MANAGEMENT_MASK= 4294965248¶
-
MERGED_POLARITY= 'MERGED'¶
-
RIGHT_RETINA= 'RIGHT'¶
-
RIGHT_RETINA_DISABLE= 70¶
-
RIGHT_RETINA_ENABLE= 70¶
-
RIGHT_RETINA_KEY_SET= 68¶
-
UP_POLARITY= 'UP'¶
-
default_parameters= {'board_address': None, 'label': 'MunichRetinaDevice', 'polarity': None}¶
-
get_outgoing_partition_constraints(partition)[source]¶ Get constraints to be added to the given edge that comes out of this vertex.
Parameters: partition (AbstractOutgoingEdgePartition) – An edge that comes out of this vertex Returns: A list of constraints Return type: list(AbstractConstraint)
-
pause_stop_commands¶ The commands needed when pausing or stopping simulation
Return type: iterable(MultiCastCommand)
-
start_resume_commands¶ The commands needed when starting or resuming simulation
Return type: iterable(MultiCastCommand)
-
timed_commands¶ The commands to be sent at given times in the simulation
Return type: iterable(MultiCastCommand)
-
-
class
spynnaker.pyNN.external_devices_models.ThresholdTypeMulticastDeviceControl(device)[source]¶ Bases:
spynnaker.pyNN.models.neuron.threshold_types.abstract_threshold_type.AbstractThresholdTypeA threshold type that can send multicast keys with the value of membrane voltage as the payload
-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
get_values(parameters, state_variables, vertex_slice)[source]¶ Get the values to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of variables being retrieved
Returns: A list with the same length as self.struct.field_types
Return type: A list of (single value or list of values or RangedList)
- parameters (
-
has_variable(variable)[source]¶ Determine if this component has a variable by the given name
Parameters: variable (str) – The name of the variable Return type: bool
-
update_values(values, parameters, state_variables)[source]¶ Update the parameters and state variables with the given struct values that have been read from the machine
Parameters: - values – The values read from the machine, one for each struct element
- parameters – The holder of the parameters to update
- state_variables – The holder of the state variables to update
-
spynnaker.pyNN.models package¶
-
class
spynnaker.pyNN.models.abstract_models.abstract_accepts_incoming_synapses.AbstractAcceptsIncomingSynapses[source]¶ Bases:
objectIndicates an object that can be a post-vertex in a PyNN projection.
-
add_pre_run_connection_holder(connection_holder, projection_edge, synapse_information)[source]¶ Add a connection holder to the vertex to be filled in when the connections are actually generated.
-
get_connections_from_machine(transceiver, placement, edge, graph_mapper, routing_infos, synapse_information, machine_time_step, using_extra_monitor_cores, placements=None, monitor_api=None, monitor_placement=None, monitor_cores=None, handle_time_out_configuration=True, fixed_routes=None)[source]¶ Get the connections from the machine post-run.
-
get_maximum_delay_supported_in_ms(machine_time_step)[source]¶ Get the maximum delay supported by this vertex.
-
get_synapse_id_by_target¶ Get the ID of a synapse given the name.
Parameters: target (str) – The name of the synapse Return type: int
-
-
class
spynnaker.pyNN.models.abstract_models.abstract_population_initializable.AbstractPopulationInitializable[source]¶ Bases:
objectIndicates that this object has properties that can be initialised by a PyNN Population
-
get_initial_value(variable, selector=None)[source]¶ Gets the value for any variable whose in initialize_parameters.keys
Should return the current value not the default one.
Must support the variable as listed in initialize_parameters.keys, ideally also with _init removed or added.
Parameters: - variable (str) – variable name with our without _init
- selector – a description of the subrange to accept. Or None for all. See: _selector_to_ids in SpiNNUtils.spinn_utilities.ranged.abstract_sized.py
Returns: A list or an Object which act like a list
-
get_initial_values(selector=None)[source]¶ A dict containing the initial values of the state variables.
Parameters: selector – a description of the subrange to accept. Or None for all. See: _selector_to_ids in SpiNNUtils.spinn_utilities.ranged.abstract_sized.py
-
initial_values¶ A dict containing the initial values of the state variables.
-
initialize(variable, value)[source]¶ Set the initial value of one of the state variables of the neurons in this population.
-
initialize_parameters¶ List the parameters that are initializable.
If “foo” is initializable there should be a setter initialize_foo and a getter property foo_init
Returns: list of property names
-
set_initial_value(variable, value, selector=None)[source]¶ Sets the value for any variable whose in initialize_parameters.keys
Must support the variable as listed in initialize_parameters.keys, ideally also with _init removed or added
Parameters: - variable (str) – variable name with our without _init
- value – New value for the variable
- selector – a description of the subrange to accept. Or None for all. See: _selector_to_ids in SpiNNUtils.spinn_utilities.ranged.abstract_sized.py
Returns: A list or an Object which act like a list
-
-
class
spynnaker.pyNN.models.abstract_models.abstract_population_settable.AbstractPopulationSettable[source]¶ Bases:
spynnaker.pyNN.models.abstract_models.abstract_settable.AbstractSettableIndicates that some properties of this object can be accessed from the PyNN population set and get methods.
-
get_value_by_selector(selector, key)[source]¶ Gets the value for a particular key but only for the selected subset.
Parameters: - selector – See RangedList.get_value_by_selector as this is just a pass through method
- key – the name of the parameter to change
- value – the new value of the parameter to assign
-
n_atoms¶ ” See ApplicationVertex.n_atoms
-
set_value_by_selector(selector, key, value)[source]¶ Sets the value for a particular key but only for the selected subset.
Parameters: - selector – See RangedList.set_value_by_selector as this is just a pass through method
- key – the name of the parameter to change
- value – the new value of the parameter to assign
-
-
class
spynnaker.pyNN.models.abstract_models.AbstractAcceptsIncomingSynapses[source]¶ Bases:
objectIndicates an object that can be a post-vertex in a PyNN projection.
-
add_pre_run_connection_holder(connection_holder, projection_edge, synapse_information)[source]¶ Add a connection holder to the vertex to be filled in when the connections are actually generated.
-
get_connections_from_machine(transceiver, placement, edge, graph_mapper, routing_infos, synapse_information, machine_time_step, using_extra_monitor_cores, placements=None, monitor_api=None, monitor_placement=None, monitor_cores=None, handle_time_out_configuration=True, fixed_routes=None)[source]¶ Get the connections from the machine post-run.
-
get_maximum_delay_supported_in_ms(machine_time_step)[source]¶ Get the maximum delay supported by this vertex.
-
get_synapse_id_by_target¶ Get the ID of a synapse given the name.
Parameters: target (str) – The name of the synapse Return type: int
-
-
class
spynnaker.pyNN.models.abstract_models.AbstractFilterableEdge[source]¶ Bases:
objectAn edge that can be filtered
-
class
spynnaker.pyNN.models.abstract_models.AbstractPopulationInitializable[source]¶ Bases:
objectIndicates that this object has properties that can be initialised by a PyNN Population
-
get_initial_value(variable, selector=None)[source]¶ Gets the value for any variable whose in initialize_parameters.keys
Should return the current value not the default one.
Must support the variable as listed in initialize_parameters.keys, ideally also with _init removed or added.
Parameters: - variable (str) – variable name with our without _init
- selector – a description of the subrange to accept. Or None for all. See: _selector_to_ids in SpiNNUtils.spinn_utilities.ranged.abstract_sized.py
Returns: A list or an Object which act like a list
-
get_initial_values(selector=None)[source]¶ A dict containing the initial values of the state variables.
Parameters: selector – a description of the subrange to accept. Or None for all. See: _selector_to_ids in SpiNNUtils.spinn_utilities.ranged.abstract_sized.py
-
initial_values¶ A dict containing the initial values of the state variables.
-
initialize(variable, value)[source]¶ Set the initial value of one of the state variables of the neurons in this population.
-
initialize_parameters¶ List the parameters that are initializable.
If “foo” is initializable there should be a setter initialize_foo and a getter property foo_init
Returns: list of property names
-
set_initial_value(variable, value, selector=None)[source]¶ Sets the value for any variable whose in initialize_parameters.keys
Must support the variable as listed in initialize_parameters.keys, ideally also with _init removed or added
Parameters: - variable (str) – variable name with our without _init
- value – New value for the variable
- selector – a description of the subrange to accept. Or None for all. See: _selector_to_ids in SpiNNUtils.spinn_utilities.ranged.abstract_sized.py
Returns: A list or an Object which act like a list
-
-
class
spynnaker.pyNN.models.abstract_models.AbstractPopulationSettable[source]¶ Bases:
spynnaker.pyNN.models.abstract_models.abstract_settable.AbstractSettableIndicates that some properties of this object can be accessed from the PyNN population set and get methods.
-
get_value_by_selector(selector, key)[source]¶ Gets the value for a particular key but only for the selected subset.
Parameters: - selector – See RangedList.get_value_by_selector as this is just a pass through method
- key – the name of the parameter to change
- value – the new value of the parameter to assign
-
n_atoms¶ ” See ApplicationVertex.n_atoms
-
set_value_by_selector(selector, key, value)[source]¶ Sets the value for a particular key but only for the selected subset.
Parameters: - selector – See RangedList.set_value_by_selector as this is just a pass through method
- key – the name of the parameter to change
- value – the new value of the parameter to assign
-
-
class
spynnaker.pyNN.models.abstract_models.AbstractReadParametersBeforeSet[source]¶ Bases:
objectA vertex whose parameters must be read before any can be set
-
class
spynnaker.pyNN.models.common.abstract_neuron_recordable.AbstractNeuronRecordable[source]¶ Bases:
objectIndicates that a variable (e.g., membrane voltage) can be recorded from this object
-
clear_recording(variable, buffer_manager, placements, graph_mapper)[source]¶ Clear the recorded data from the object
Parameters: - buffer_manager – the buffer manager object
- placements – the placements object
- graph_mapper – the graph mapper object
Return type: None
-
get_data(variable, n_machine_time_steps, placements, graph_mapper, buffer_manager, machine_time_step)[source]¶ Get the recorded data
Parameters: - variable –
- n_machine_time_steps –
- placements –
- graph_mapper –
- buffer_manager –
- machine_time_step –
Returns:
-
get_neuron_sampling_interval(variable)[source]¶ Returns the current sampling interval for this variable
Parameters: variable – PyNN name of the variable Returns: Sampling interval in micro seconds
-
get_recordable_variables()[source]¶ Returns a list of the variables this models is expected to collect
-
-
class
spynnaker.pyNN.models.common.abstract_spike_recordable.AbstractSpikeRecordable[source]¶ Bases:
objectIndicates that spikes can be recorded from this object
-
clear_spike_recording(buffer_manager, placements, graph_mapper)[source]¶ Clear the recorded data from the object
Parameters: - buffer_manager – the buffer manager object
- placements – the placements object
- graph_mapper – the graph mapper object
Return type: None
-
get_spikes(placements, graph_mapper, buffer_manager, machine_time_step)[source]¶ Get the recorded spikes from the object
Parameters: - placements – the placements object
- graph_mapper – the graph mapper object
- buffer_manager – the buffer manager object
- machine_time_step – the time step of the simulation
Returns: A numpy array of 2-element arrays of (neuron_id, time) ordered by time
-
get_spikes_sampling_interval()[source]¶ Return the current sampling interval for spikes
Returns: Sampling interval in micro seconds
-
is_recording_spikes()[source]¶ Determine if spikes are being recorded
Returns: True if spikes are being recorded, False otherwise Return type: bool
-
set_recording_spikes(new_state=True, sampling_interval=None, indexes=None)[source]¶ Set spikes to being recorded. If new_state is false all other parameters are ignored.
Parameters: - new_state (bool) – Set if the spikes are recording or not
- sampling_interval – The interval at which spikes are recorded. Must be a whole multiple of the timestep None will be taken as the timestep
- indexes – The indexes of the neurons that will record spikes. If None the assumption is all neurons are recording
-
-
class
spynnaker.pyNN.models.common.neuron_recorder.NeuronRecorder(allowed_variables, n_neurons)[source]¶ Bases:
object-
MAX_RATE= 4294967295¶
-
N_BYTES_FOR_TIMESTAMP= 4¶
-
N_BYTES_PER_INDEX= 1¶
-
N_BYTES_PER_POINTER= 4¶
-
N_BYTES_PER_RATE= 4¶
-
N_BYTES_PER_SIZE= 4¶
-
N_BYTES_PER_VALUE= 4¶
-
N_BYTES_PER_WORD= 4¶
-
N_CPU_CYCLES_PER_NEURON= 8¶
-
SARK_BLOCK_SIZE= 8¶
-
get_buffered_sdram(variable, vertex_slice, n_machine_time_steps)[source]¶ Returns the SDRAM used for this may timesteps
If required the total is rounded up so the space will always fit
Parameters: - variable – The
- vertex_slice –
Returns:
-
get_buffered_sdram_per_record(variable, vertex_slice)[source]¶ Return the SDRAM used per record
Parameters: - variable –
- vertex_slice –
Returns:
-
get_buffered_sdram_per_timestep(variable, vertex_slice)[source]¶ Return the SDRAM used per timestep.
In the case where sampling is used it returns the average for recording and none recording based on the recording rate
Parameters: - variable –
- vertex_slice –
Returns:
-
get_matrix_data(label, buffer_manager, region, placements, graph_mapper, application_vertex, variable, n_machine_time_steps)[source]¶ Read a uint32 mapped to time and neuron IDs from the SpiNNaker machine.
Parameters: - label – vertex label
- buffer_manager – the manager for buffered data
- region – the DSG region ID used for this data
- placements – the placements object
- graph_mapper – the mapping between application and machine vertices
- application_vertex –
- variable (str) – PyNN name for the variable (V, gsy_inh etc.)
- n_machine_time_steps –
Returns:
-
get_neuron_sampling_interval(variable)[source]¶ Return the current sampling interval for this variable
Parameters: variable – PyNN name of the variable Returns: Sampling interval in micro seconds
-
get_sampling_overflow_sdram(vertex_slice)[source]¶ Get the extra SDRAM that should be reserved if using per_timestep
This is the extra that must be reserved if per_timestep is an average rather than fixed for every timestep.
When sampling the average * time_steps may not be quite enough. This returns the extra space in the worst case where time_steps is a multiple of sampling rate + 1, and recording is done in the first and last time_step
Parameters: vertex_slice – Returns: Highest possible overflow needed
-
get_spikes(label, buffer_manager, region, placements, graph_mapper, application_vertex, machine_time_step)[source]¶
-
recorded_region_ids¶
-
recording_variables¶
-
-
spynnaker.pyNN.models.common.recording_utils.get_buffer_sizes(buffer_max, space_needed, enable_buffered_recording)[source]¶
-
spynnaker.pyNN.models.common.recording_utils.get_data(transceiver, placement, region, region_size)[source]¶ Get the recorded data from a region
-
spynnaker.pyNN.models.common.recording_utils.get_recording_region_size_in_bytes(n_machine_time_steps, bytes_per_timestep)[source]¶ Get the size of a recording region in bytes
-
class
spynnaker.pyNN.models.common.simple_population_settable.SimplePopulationSettable[source]¶ Bases:
spynnaker.pyNN.models.abstract_models.abstract_population_settable.AbstractPopulationSettableAn object all of whose properties can be accessed from a PyNN Population i.e. no properties are hidden
-
class
spynnaker.pyNN.models.common.AbstractNeuronRecordable[source]¶ Bases:
objectIndicates that a variable (e.g., membrane voltage) can be recorded from this object
-
clear_recording(variable, buffer_manager, placements, graph_mapper)[source]¶ Clear the recorded data from the object
Parameters: - buffer_manager – the buffer manager object
- placements – the placements object
- graph_mapper – the graph mapper object
Return type: None
-
get_data(variable, n_machine_time_steps, placements, graph_mapper, buffer_manager, machine_time_step)[source]¶ Get the recorded data
Parameters: - variable –
- n_machine_time_steps –
- placements –
- graph_mapper –
- buffer_manager –
- machine_time_step –
Returns:
-
get_neuron_sampling_interval(variable)[source]¶ Returns the current sampling interval for this variable
Parameters: variable – PyNN name of the variable Returns: Sampling interval in micro seconds
-
get_recordable_variables()[source]¶ Returns a list of the variables this models is expected to collect
-
-
class
spynnaker.pyNN.models.common.AbstractSpikeRecordable[source]¶ Bases:
objectIndicates that spikes can be recorded from this object
-
clear_spike_recording(buffer_manager, placements, graph_mapper)[source]¶ Clear the recorded data from the object
Parameters: - buffer_manager – the buffer manager object
- placements – the placements object
- graph_mapper – the graph mapper object
Return type: None
-
get_spikes(placements, graph_mapper, buffer_manager, machine_time_step)[source]¶ Get the recorded spikes from the object
Parameters: - placements – the placements object
- graph_mapper – the graph mapper object
- buffer_manager – the buffer manager object
- machine_time_step – the time step of the simulation
Returns: A numpy array of 2-element arrays of (neuron_id, time) ordered by time
-
get_spikes_sampling_interval()[source]¶ Return the current sampling interval for spikes
Returns: Sampling interval in micro seconds
-
is_recording_spikes()[source]¶ Determine if spikes are being recorded
Returns: True if spikes are being recorded, False otherwise Return type: bool
-
set_recording_spikes(new_state=True, sampling_interval=None, indexes=None)[source]¶ Set spikes to being recorded. If new_state is false all other parameters are ignored.
Parameters: - new_state (bool) – Set if the spikes are recording or not
- sampling_interval – The interval at which spikes are recorded. Must be a whole multiple of the timestep None will be taken as the timestep
- indexes – The indexes of the neurons that will record spikes. If None the assumption is all neurons are recording
-
-
class
spynnaker.pyNN.models.common.EIEIOSpikeRecorder[source]¶ Bases:
objectRecords spikes using EIEIO format
-
get_spikes(label, buffer_manager, region, placements, graph_mapper, application_vertex, base_key_function, machine_time_step)[source]¶
-
record¶
-
-
class
spynnaker.pyNN.models.common.NeuronRecorder(allowed_variables, n_neurons)[source]¶ Bases:
object-
MAX_RATE= 4294967295¶
-
N_BYTES_FOR_TIMESTAMP= 4¶
-
N_BYTES_PER_INDEX= 1¶
-
N_BYTES_PER_POINTER= 4¶
-
N_BYTES_PER_RATE= 4¶
-
N_BYTES_PER_SIZE= 4¶
-
N_BYTES_PER_VALUE= 4¶
-
N_BYTES_PER_WORD= 4¶
-
N_CPU_CYCLES_PER_NEURON= 8¶
-
SARK_BLOCK_SIZE= 8¶
-
get_buffered_sdram(variable, vertex_slice, n_machine_time_steps)[source]¶ Returns the SDRAM used for this may timesteps
If required the total is rounded up so the space will always fit
Parameters: - variable – The
- vertex_slice –
Returns:
-
get_buffered_sdram_per_record(variable, vertex_slice)[source]¶ Return the SDRAM used per record
Parameters: - variable –
- vertex_slice –
Returns:
-
get_buffered_sdram_per_timestep(variable, vertex_slice)[source]¶ Return the SDRAM used per timestep.
In the case where sampling is used it returns the average for recording and none recording based on the recording rate
Parameters: - variable –
- vertex_slice –
Returns:
-
get_matrix_data(label, buffer_manager, region, placements, graph_mapper, application_vertex, variable, n_machine_time_steps)[source]¶ Read a uint32 mapped to time and neuron IDs from the SpiNNaker machine.
Parameters: - label – vertex label
- buffer_manager – the manager for buffered data
- region – the DSG region ID used for this data
- placements – the placements object
- graph_mapper – the mapping between application and machine vertices
- application_vertex –
- variable (str) – PyNN name for the variable (V, gsy_inh etc.)
- n_machine_time_steps –
Returns:
-
get_neuron_sampling_interval(variable)[source]¶ Return the current sampling interval for this variable
Parameters: variable – PyNN name of the variable Returns: Sampling interval in micro seconds
-
get_sampling_overflow_sdram(vertex_slice)[source]¶ Get the extra SDRAM that should be reserved if using per_timestep
This is the extra that must be reserved if per_timestep is an average rather than fixed for every timestep.
When sampling the average * time_steps may not be quite enough. This returns the extra space in the worst case where time_steps is a multiple of sampling rate + 1, and recording is done in the first and last time_step
Parameters: vertex_slice – Returns: Highest possible overflow needed
-
get_spikes(label, buffer_manager, region, placements, graph_mapper, application_vertex, machine_time_step)[source]¶
-
recorded_region_ids¶
-
recording_variables¶
-
-
class
spynnaker.pyNN.models.common.MultiSpikeRecorder[source]¶ Bases:
object-
get_spikes(label, buffer_manager, region, placements, graph_mapper, application_vertex, machine_time_step)[source]¶
-
record¶
-
-
class
spynnaker.pyNN.models.common.SimplePopulationSettable[source]¶ Bases:
spynnaker.pyNN.models.abstract_models.abstract_population_settable.AbstractPopulationSettableAn object all of whose properties can be accessed from a PyNN Population i.e. no properties are hidden
-
spynnaker.pyNN.models.common.get_buffer_sizes(buffer_max, space_needed, enable_buffered_recording)[source]¶
-
spynnaker.pyNN.models.common.get_data(transceiver, placement, region, region_size)[source]¶ Get the recorded data from a region
-
spynnaker.pyNN.models.common.needs_buffering(buffer_max, space_needed, enable_buffered_recording)[source]¶
-
class
spynnaker.pyNN.models.neural_projections.connectors.abstract_connector.AbstractConnector(safe=True, verbose=False, rng=None)[source]¶ Bases:
objectAbstract class that all PyNN Connectors extend.
-
NUMPY_SYNAPSES_DTYPE= [('source', 'uint32'), ('target', 'uint16'), ('weight', 'float64'), ('delay', 'float64'), ('synapse_type', 'uint8')]¶
-
create_synaptic_block(weights, delays, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Create a synaptic block from the data.
-
get_delay_maximum(delays)[source]¶ Get the maximum delay specified by the user in ms, or None if unbounded.
-
get_n_connections_from_pre_vertex_maximum(delays, post_vertex_slice, min_delay=None, max_delay=None)[source]¶ Get the maximum number of connections between those from any neuron in the pre vertex to the neurons in the post_vertex_slice, for connections with a delay between min_delay and max_delay (inclusive) if both specified (otherwise all connections).
-
get_n_connections_to_post_vertex_maximum()[source]¶ Get the maximum number of connections between those to any neuron in the post vertex from neurons in the pre vertex.
-
post_population¶
-
pre_population¶
-
safe¶
-
set_space(space)[source]¶ Set the space object (allowed after instantiation).
Parameters: space – Returns:
-
space¶
-
verbose¶
-
-
class
spynnaker.pyNN.models.neural_projections.connectors.abstract_generate_connector_on_machine.AbstractGenerateConnectorOnMachine(safe=True, verbose=False)[source]¶ Bases:
spynnaker.pyNN.models.neural_projections.connectors.abstract_connector.AbstractConnectorIndicates that the connectivity can be generated on the machine
-
gen_connector_id¶ Get the id of the connection generator on the machine
Return type: int
-
gen_connector_params(pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Get the parameters of the on machine generation.
Return type: numpy array of uint32
-
gen_connector_params_size_in_bytes¶ The size of the connector parameters in bytes.
Return type: int
-
gen_delay_params(delays, pre_vertex_slice, post_vertex_slice)[source]¶ Get the parameters of the delay generator on the machine
Return type: numpy array of uint32
-
gen_delay_params_size_in_bytes(delays)[source]¶ The size of the delay parameters in bytes
Return type: int
-
gen_weight_params_size_in_bytes(weights)[source]¶ The size of the weight parameters in bytes
Return type: int
-
-
class
spynnaker.pyNN.models.neural_projections.connectors.abstract_generate_connector_on_machine.ConnectorIDs[source]¶ Bases:
enum.EnumAn enumeration.
-
ALL_TO_ALL_CONNECTOR= 1¶
-
FIXED_NUMBER_POST_CONNECTOR= 5¶
-
FIXED_NUMBER_PRE_CONNECTOR= 4¶
-
FIXED_PROBABILITY_CONNECTOR= 2¶
-
FIXED_TOTAL_NUMBER_CONNECTOR= 3¶
-
KERNEL_CONNECTOR= 6¶
-
ONE_TO_ONE_CONNECTOR= 0¶
-
-
class
spynnaker.pyNN.models.neural_projections.connectors.all_to_all_connector.AllToAllConnector(allow_self_connections=True, safe=True, verbose=None)[source]¶ -
Connects all cells in the presynaptic population to all cells in the postsynaptic population.
Parameters: allow_self_connections (bool) – if the connector is used to connect a Population to itself, this flag determines whether a neuron is allowed to connect to itself, or only to other neurons in the Population. -
allow_self_connections¶
-
create_synaptic_block(weights, delays, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Create a synaptic block from the data.
-
gen_connector_id¶ Get the id of the connection generator on the machine
Return type: int
-
gen_connector_params(pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Get the parameters of the on machine generation.
Return type: numpy array of uint32
-
gen_connector_params_size_in_bytes¶ The size of the connector parameters in bytes.
Return type: int
-
get_delay_maximum(delays)[source]¶ Get the maximum delay specified by the user in ms, or None if unbounded.
-
get_n_connections_from_pre_vertex_maximum(delays, post_vertex_slice, min_delay=None, max_delay=None)[source]¶ Get the maximum number of connections between those from any neuron in the pre vertex to the neurons in the post_vertex_slice, for connections with a delay between min_delay and max_delay (inclusive) if both specified (otherwise all connections).
-
-
class
spynnaker.pyNN.models.neural_projections.connectors.array_connector.ArrayConnector(array, safe=True, callback=None, verbose=False)[source]¶ Bases:
spynnaker.pyNN.models.neural_projections.connectors.abstract_connector.AbstractConnectorMake connections using an array of integers based on the IDs of the neurons in the pre- and post-populations.
Parameters: array – An explicit boolean matrix that specifies the connections between the pre- and post-populations (see PyNN documentation) -
create_synaptic_block(weights, delays, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Create a synaptic block from the data.
-
get_delay_maximum(delays)[source]¶ Get the maximum delay specified by the user in ms, or None if unbounded.
-
get_n_connections_from_pre_vertex_maximum(delays, post_vertex_slice, min_delay=None, max_delay=None)[source]¶ Get the maximum number of connections between those from any neuron in the pre vertex to the neurons in the post_vertex_slice, for connections with a delay between min_delay and max_delay (inclusive) if both specified (otherwise all connections).
-
-
class
spynnaker.pyNN.models.neural_projections.connectors.csa_connector.CSAConnector(cset, safe=True, callback=None, verbose=False)[source]¶ Bases:
spynnaker.pyNN.models.neural_projections.connectors.abstract_connector.AbstractConnectorMake connections using a Connection Set Algebra (Djurfeldt 2012) description between the neurons in the pre- and post-populations. If you get TypeError in Python 3 see: https://github.com/INCF/csa/issues/10
Parameters: cset ('?') – A description of the connection set between populations -
create_synaptic_block(weights, delays, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Create a synaptic block from the data.
-
get_delay_maximum(delays)[source]¶ Get the maximum delay specified by the user in ms, or None if unbounded.
-
get_n_connections_from_pre_vertex_maximum(delays, post_vertex_slice, min_delay=None, max_delay=None)[source]¶ Get the maximum number of connections between those from any neuron in the pre vertex to the neurons in the post_vertex_slice, for connections with a delay between min_delay and max_delay (inclusive) if both specified (otherwise all connections).
-
-
class
spynnaker.pyNN.models.neural_projections.connectors.distance_dependent_probability_connector.DistanceDependentProbabilityConnector(d_expression, allow_self_connections=True, safe=True, verbose=False, n_connections=None, rng=None)[source]¶ Bases:
spynnaker.pyNN.models.neural_projections.connectors.abstract_connector.AbstractConnectorMake connections using a distribution which varies with distance.
Parameters: - d_expression (bool) – the right-hand side of a valid python expression for probability, involving ‘d’, e.g. “exp(-abs(d))”, or “d<3”, that can be parsed by eval(), that computes the distance dependent distribution.
- allow_self_connections – if the connector is used to connect a Population to itself, this flag determines whether a neuron is allowed to connect to itself, or only to other neurons in the Population.
- space (pyNN.Space) – a Space object, needed if you wish to specify distance-dependent weights or delays.
- n_connections (int or None) – The number of efferent synaptic connections per neuron.
-
allow_self_connections¶
-
create_synaptic_block(weights, delays, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Create a synaptic block from the data.
-
d_expression¶
-
get_delay_maximum(delays)[source]¶ Get the maximum delay specified by the user in ms, or None if unbounded.
-
get_n_connections_from_pre_vertex_maximum(delays, post_vertex_slice, min_delay=None, max_delay=None)[source]¶ Get the maximum number of connections between those from any neuron in the pre vertex to the neurons in the post_vertex_slice, for connections with a delay between min_delay and max_delay (inclusive) if both specified (otherwise all connections).
-
class
spynnaker.pyNN.models.neural_projections.connectors.fixed_number_post_connector.FixedNumberPostConnector(n, allow_self_connections=True, with_replacement=False, safe=True, verbose=False, rng=None)[source]¶ -
Connects a fixed number of post-synaptic neurons selected at random, to all pre-synaptic neurons.
Parameters: - n (int) – number of random post-synaptic neurons connected to pre-neurons.
- allow_self_connections (bool) – if the connector is used to connect a Population to itself, this flag determines whether a neuron is allowed to connect to itself, or only to other neurons in the Population.
- with_replacement (bool) – this flag determines how the random selection of post-synaptic neurons is performed; if true, then every post-synaptic neuron can be chosen on each occasion, and so multiple connections between neuron pairs are possible; if false, then once a post-synaptic neuron has been connected to a pre-neuron, it can’t be connected again.
-
allow_self_connections¶
-
create_synaptic_block(weights, delays, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Create a synaptic block from the data.
-
gen_connector_id¶ Get the id of the connection generator on the machine
Return type: int
-
gen_connector_params(pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Get the parameters of the on machine generation.
Return type: numpy array of uint32
-
gen_connector_params_size_in_bytes¶ The size of the connector parameters in bytes.
Return type: int
-
get_delay_maximum(delays)[source]¶ Get the maximum delay specified by the user in ms, or None if unbounded.
-
get_n_connections_from_pre_vertex_maximum(delays, post_vertex_slice, min_delay=None, max_delay=None)[source]¶ Get the maximum number of connections between those from any neuron in the pre vertex to the neurons in the post_vertex_slice, for connections with a delay between min_delay and max_delay (inclusive) if both specified (otherwise all connections).
-
class
spynnaker.pyNN.models.neural_projections.connectors.fixed_number_pre_connector.FixedNumberPreConnector(n, allow_self_connections=True, with_replacement=False, safe=True, verbose=False, rng=None)[source]¶ -
Connects a fixed number of pre-synaptic neurons selected at random, to all post-synaptic neurons.
Parameters: - n (int) – number of random pre-synaptic neurons connected to output
- allow_self_connections (bool) – if the connector is used to connect a Population to itself, this flag determines whether a neuron is allowed to connect to itself, or only to other neurons in the Population.
- with_replacement (bool) – this flag determines how the random selection of pre-synaptic neurons is performed; if true, then every pre-synaptic neuron can be chosen on each occasion, and so multiple connections between neuron pairs are possible; if false, then once a pre-synaptic neuron has been connected to a post-neuron, it can’t be connected again.
-
allow_self_connections¶
-
create_synaptic_block(weights, delays, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Create a synaptic block from the data.
-
gen_connector_id¶ Get the id of the connection generator on the machine
Return type: int
-
gen_connector_params(pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Get the parameters of the on machine generation.
Return type: numpy array of uint32
-
gen_connector_params_size_in_bytes¶ The size of the connector parameters in bytes.
Return type: int
-
get_delay_maximum(delays)[source]¶ Get the maximum delay specified by the user in ms, or None if unbounded.
-
get_n_connections_from_pre_vertex_maximum(delays, post_vertex_slice, min_delay=None, max_delay=None)[source]¶ Get the maximum number of connections between those from any neuron in the pre vertex to the neurons in the post_vertex_slice, for connections with a delay between min_delay and max_delay (inclusive) if both specified (otherwise all connections).
-
class
spynnaker.pyNN.models.neural_projections.connectors.fixed_probability_connector.FixedProbabilityConnector(p_connect, allow_self_connections=True, safe=True, verbose=False, rng=None)[source]¶ -
For each pair of pre-post cells, the connection probability is constant.
Parameters: - p_connect (float) – a float between zero and one. Each potential connection is created with this probability.
- allow_self_connections (bool) – if the connector is used to connect a Population to itself, this flag determines whether a neuron is allowed to connect to itself, or only to other neurons in the Population.
- space (pyNN.Space) – a Space object, needed if you wish to specify distance-dependent weights or delays - not implemented
-
create_synaptic_block(weights, delays, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Create a synaptic block from the data.
-
gen_connector_id¶ Get the id of the connection generator on the machine
Return type: int
-
gen_connector_params(pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Get the parameters of the on machine generation.
Return type: numpy array of uint32
-
gen_connector_params_size_in_bytes¶ The size of the connector parameters in bytes.
Return type: int
-
get_delay_maximum(delays)[source]¶ Get the maximum delay specified by the user in ms, or None if unbounded.
-
get_n_connections_from_pre_vertex_maximum(delays, post_vertex_slice, min_delay=None, max_delay=None)[source]¶ Get the maximum number of connections between those from any neuron in the pre vertex to the neurons in the post_vertex_slice, for connections with a delay between min_delay and max_delay (inclusive) if both specified (otherwise all connections).
-
class
spynnaker.pyNN.models.neural_projections.connectors.from_list_connector.FromListConnector(conn_list, safe=True, verbose=False, column_names=None)[source]¶ Bases:
spynnaker.pyNN.models.neural_projections.connectors.abstract_connector.AbstractConnectorMake connections according to a list.
Param: conn_list: a list of tuples, one tuple for each connection. Each tuple should contain at least:
(pre_idx, post_idx)
where pre_idx is the index (i.e. order in the Population, not the ID) of the presynaptic neuron, and post_idx is the index of the postsynaptic neuron.
Additional items per synapse are acceptable but all synapses should have the same number of items.
-
column_names¶
-
conn_list¶
-
create_synaptic_block(weights, delays, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Create a synaptic block from the data.
-
get_delay_maximum(delays)[source]¶ Get the maximum delay specified by the user in ms, or None if unbounded.
-
get_n_connections_from_pre_vertex_maximum(delays, post_vertex_slice, min_delay=None, max_delay=None)[source]¶ Get the maximum number of connections between those from any neuron in the pre vertex to the neurons in the post_vertex_slice, for connections with a delay between min_delay and max_delay (inclusive) if both specified (otherwise all connections).
-
-
class
spynnaker.pyNN.models.neural_projections.connectors.index_based_probability_connector.IndexBasedProbabilityConnector(index_expression, allow_self_connections=True, rng=None, safe=True, callback=None, verbose=False)[source]¶ Bases:
spynnaker.pyNN.models.neural_projections.connectors.abstract_connector.AbstractConnectorMake connections using a probability distribution which varies dependent upon the indices of the pre- and post-populations.
Parameters: - index_expression (string) – the right-hand side of a valid python expression for probability, involving the indices of the pre and post populations, that can be parsed by eval(), that computes a probability dist.
- allow_self_connections (bool) – if the connector is used to connect a Population to itself, this flag determines whether a neuron is allowed to connect to itself, or only to other neurons in the Population.
-
allow_self_connections¶
-
create_synaptic_block(weights, delays, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Create a synaptic block from the data.
-
get_delay_maximum(delays)[source]¶ Get the maximum delay specified by the user in ms, or None if unbounded.
-
get_n_connections_from_pre_vertex_maximum(delays, post_vertex_slice, min_delay=None, max_delay=None)[source]¶ Get the maximum number of connections between those from any neuron in the pre vertex to the neurons in the post_vertex_slice, for connections with a delay between min_delay and max_delay (inclusive) if both specified (otherwise all connections).
-
get_n_connections_to_post_vertex_maximum()[source]¶ Get the maximum number of connections between those to any neuron in the post vertex from neurons in the pre vertex.
-
index_expression¶
-
class
spynnaker.pyNN.models.neural_projections.connectors.kernel_connector.ConvolutionKernel[source]¶ Bases:
numpy.ndarray
-
class
spynnaker.pyNN.models.neural_projections.connectors.kernel_connector.KernelConnector(shape_pre, shape_post, shape_kernel, weight_kernel, delay_kernel, shape_common, pre_sample_steps, pre_start_coords, post_sample_steps, post_start_coords, safe, space, verbose)[source]¶ -
Where the pre- and post-synaptic populations are considered as a 2D array. Connect every post(row, col) neuron to many pre(row, col, kernel) through a (kernel) set of weights and/or delays.
TODO: should these include allow_self_connections and with_replacement?
Parameters: - shape_pre – 2D shape of the pre population (rows/height, cols/width, usually the input image shape)
- shape_post – 2D shape of the post population (rows/height, cols/width)
- shape_kernel – 2D shape of the kernel (rows/height, cols/width)
- (optional) (pre/post_start_coords) – 2D matrix of size shape_kernel describing the weights
- (optional) – 2D matrix of size shape_kernel describing the delays
- (optional) – 2D shape of common coordinate system (for both pre and post, usually the input image sizes)
- (optional) – Sampling steps/jumps for pre/post pop <=> (startX, endX, _stepX_) None or 2-item array
- (optional) – Starting row/col for pre/post sampling <=> (_startX_, endX, stepX) None or 2-item array
-
create_synaptic_block(weights, delays, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Create a synaptic block from the data.
-
gen_connector_id¶ Get the id of the connection generator on the machine
Return type: int
-
gen_connector_params(pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Get the parameters of the on machine generation.
Return type: numpy array of uint32
-
gen_connector_params_size_in_bytes¶ The size of the connector parameters in bytes.
Return type: int
-
gen_delay_params(delays, pre_vertex_slice, post_vertex_slice)[source]¶ Get the parameters of the delay generator on the machine
Return type: numpy array of uint32
-
gen_delay_params_size_in_bytes(delays)[source]¶ The size of the delay parameters in bytes
Return type: int
-
gen_weight_params_size_in_bytes(weights)[source]¶ The size of the weight parameters in bytes
Return type: int
-
gen_weights_params(weights, pre_vertex_slice, post_vertex_slice)[source]¶ Get the parameters of the weight generator on the machine
Return type: numpy array of uint32
-
generate_on_machine¶ Determine if this instance can generate on the machine.
Default implementation returns True if the weights and delays can be generated on the machine
Return type: bool
-
get_delay_maximum(delays)[source]¶ Get the maximum delay specified by the user in ms, or None if unbounded.
-
get_n_connections_from_pre_vertex_maximum(delays, post_vertex_slice, min_delay=None, max_delay=None)[source]¶ Get the maximum number of connections between those from any neuron in the pre vertex to the neurons in the post_vertex_slice, for connections with a delay between min_delay and max_delay (inclusive) if both specified (otherwise all connections).
-
class
spynnaker.pyNN.models.neural_projections.connectors.multapse_connector.MultapseConnector(num_synapses, allow_self_connections=True, with_replacement=True, safe=True, verbose=False, rng=None)[source]¶ -
Create a multapse connector. The size of the source and destination populations are obtained when the projection is connected. The number of synapses is specified. when instantiated, the required number of synapses is created by selecting at random from the source and target populations with replacement. Uniform selection probability is assumed.
Parameters: - num_synapses (int) – This is the total number of synapses in the connection.
- allow_self_connections (bool) – Allow a neuron to connect to itself or not.
- with_replacement (bool) – When selecting, allow a neuron to be re-selected or not.
-
create_synaptic_block(weights, delays, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Create a synaptic block from the data.
-
gen_connector_id¶ Get the id of the connection generator on the machine
Return type: int
-
gen_connector_params(pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Get the parameters of the on machine generation.
Return type: numpy array of uint32
-
gen_connector_params_size_in_bytes¶ The size of the connector parameters in bytes.
Return type: int
-
get_delay_maximum(delays)[source]¶ Get the maximum delay specified by the user in ms, or None if unbounded.
-
get_n_connections_from_pre_vertex_maximum(delays, post_vertex_slice, min_delay=None, max_delay=None)[source]¶ Get the maximum number of connections between those from any neuron in the pre vertex to the neurons in the post_vertex_slice, for connections with a delay between min_delay and max_delay (inclusive) if both specified (otherwise all connections).
-
class
spynnaker.pyNN.models.neural_projections.connectors.one_to_one_connector.OneToOneConnector(random_number_class, safe=True, verbose=False)[source]¶ -
Where the pre- and postsynaptic populations have the same size, connect cell i in the presynaptic pynn_population.py to cell i in the postsynaptic pynn_population.py for all i.
-
create_synaptic_block(weights, delays, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Create a synaptic block from the data.
-
gen_connector_id¶ Get the id of the connection generator on the machine
Return type: int
-
get_delay_maximum(delays)[source]¶ Get the maximum delay specified by the user in ms, or None if unbounded.
-
get_n_connections_from_pre_vertex_maximum(delays, post_vertex_slice, min_delay=None, max_delay=None)[source]¶ Get the maximum number of connections between those from any neuron in the pre vertex to the neurons in the post_vertex_slice, for connections with a delay between min_delay and max_delay (inclusive) if both specified (otherwise all connections).
-
-
class
spynnaker.pyNN.models.neural_projections.connectors.small_world_connector.SmallWorldConnector(degree, rewiring, allow_self_connections=True, safe=True, verbose=False, n_connections=None)[source]¶ Bases:
spynnaker.pyNN.models.neural_projections.connectors.abstract_connector.AbstractConnector-
create_synaptic_block(weights, delays, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Create a synaptic block from the data.
-
get_delay_maximum(delays)[source]¶ Get the maximum delay specified by the user in ms, or None if unbounded.
-
get_n_connections_from_pre_vertex_maximum(delays, post_vertex_slice, min_delay=None, max_delay=None)[source]¶ Get the maximum number of connections between those from any neuron in the pre vertex to the neurons in the post_vertex_slice, for connections with a delay between min_delay and max_delay (inclusive) if both specified (otherwise all connections).
-
-
class
spynnaker.pyNN.models.neural_projections.connectors.AbstractConnector(safe=True, verbose=False, rng=None)[source]¶ Bases:
objectAbstract class that all PyNN Connectors extend.
-
NUMPY_SYNAPSES_DTYPE= [('source', 'uint32'), ('target', 'uint16'), ('weight', 'float64'), ('delay', 'float64'), ('synapse_type', 'uint8')]¶
-
create_synaptic_block(weights, delays, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Create a synaptic block from the data.
-
get_delay_maximum(delays)[source]¶ Get the maximum delay specified by the user in ms, or None if unbounded.
-
get_n_connections_from_pre_vertex_maximum(delays, post_vertex_slice, min_delay=None, max_delay=None)[source]¶ Get the maximum number of connections between those from any neuron in the pre vertex to the neurons in the post_vertex_slice, for connections with a delay between min_delay and max_delay (inclusive) if both specified (otherwise all connections).
-
get_n_connections_to_post_vertex_maximum()[source]¶ Get the maximum number of connections between those to any neuron in the post vertex from neurons in the pre vertex.
-
post_population¶
-
pre_population¶
-
safe¶
-
set_space(space)[source]¶ Set the space object (allowed after instantiation).
Parameters: space – Returns:
-
space¶
-
verbose¶
-
-
class
spynnaker.pyNN.models.neural_projections.connectors.AbstractGenerateConnectorOnMachine(safe=True, verbose=False)[source]¶ Bases:
spynnaker.pyNN.models.neural_projections.connectors.abstract_connector.AbstractConnectorIndicates that the connectivity can be generated on the machine
-
gen_connector_id¶ Get the id of the connection generator on the machine
Return type: int
-
gen_connector_params(pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Get the parameters of the on machine generation.
Return type: numpy array of uint32
-
gen_connector_params_size_in_bytes¶ The size of the connector parameters in bytes.
Return type: int
-
gen_delay_params(delays, pre_vertex_slice, post_vertex_slice)[source]¶ Get the parameters of the delay generator on the machine
Return type: numpy array of uint32
-
gen_delay_params_size_in_bytes(delays)[source]¶ The size of the delay parameters in bytes
Return type: int
-
gen_weight_params_size_in_bytes(weights)[source]¶ The size of the weight parameters in bytes
Return type: int
-
-
class
spynnaker.pyNN.models.neural_projections.connectors.AllToAllConnector(allow_self_connections=True, safe=True, verbose=None)[source]¶ -
Connects all cells in the presynaptic population to all cells in the postsynaptic population.
Parameters: allow_self_connections (bool) – if the connector is used to connect a Population to itself, this flag determines whether a neuron is allowed to connect to itself, or only to other neurons in the Population. -
allow_self_connections¶
-
create_synaptic_block(weights, delays, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Create a synaptic block from the data.
-
gen_connector_id¶ Get the id of the connection generator on the machine
Return type: int
-
gen_connector_params(pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Get the parameters of the on machine generation.
Return type: numpy array of uint32
-
gen_connector_params_size_in_bytes¶ The size of the connector parameters in bytes.
Return type: int
-
get_delay_maximum(delays)[source]¶ Get the maximum delay specified by the user in ms, or None if unbounded.
-
get_n_connections_from_pre_vertex_maximum(delays, post_vertex_slice, min_delay=None, max_delay=None)[source]¶ Get the maximum number of connections between those from any neuron in the pre vertex to the neurons in the post_vertex_slice, for connections with a delay between min_delay and max_delay (inclusive) if both specified (otherwise all connections).
-
-
class
spynnaker.pyNN.models.neural_projections.connectors.ArrayConnector(array, safe=True, callback=None, verbose=False)[source]¶ Bases:
spynnaker.pyNN.models.neural_projections.connectors.abstract_connector.AbstractConnectorMake connections using an array of integers based on the IDs of the neurons in the pre- and post-populations.
Parameters: array – An explicit boolean matrix that specifies the connections between the pre- and post-populations (see PyNN documentation) -
create_synaptic_block(weights, delays, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Create a synaptic block from the data.
-
get_delay_maximum(delays)[source]¶ Get the maximum delay specified by the user in ms, or None if unbounded.
-
get_n_connections_from_pre_vertex_maximum(delays, post_vertex_slice, min_delay=None, max_delay=None)[source]¶ Get the maximum number of connections between those from any neuron in the pre vertex to the neurons in the post_vertex_slice, for connections with a delay between min_delay and max_delay (inclusive) if both specified (otherwise all connections).
-
-
class
spynnaker.pyNN.models.neural_projections.connectors.CSAConnector(cset, safe=True, callback=None, verbose=False)[source]¶ Bases:
spynnaker.pyNN.models.neural_projections.connectors.abstract_connector.AbstractConnectorMake connections using a Connection Set Algebra (Djurfeldt 2012) description between the neurons in the pre- and post-populations. If you get TypeError in Python 3 see: https://github.com/INCF/csa/issues/10
Parameters: cset ('?') – A description of the connection set between populations -
create_synaptic_block(weights, delays, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Create a synaptic block from the data.
-
get_delay_maximum(delays)[source]¶ Get the maximum delay specified by the user in ms, or None if unbounded.
-
get_n_connections_from_pre_vertex_maximum(delays, post_vertex_slice, min_delay=None, max_delay=None)[source]¶ Get the maximum number of connections between those from any neuron in the pre vertex to the neurons in the post_vertex_slice, for connections with a delay between min_delay and max_delay (inclusive) if both specified (otherwise all connections).
-
-
class
spynnaker.pyNN.models.neural_projections.connectors.DistanceDependentProbabilityConnector(d_expression, allow_self_connections=True, safe=True, verbose=False, n_connections=None, rng=None)[source]¶ Bases:
spynnaker.pyNN.models.neural_projections.connectors.abstract_connector.AbstractConnectorMake connections using a distribution which varies with distance.
Parameters: - d_expression (bool) – the right-hand side of a valid python expression for probability, involving ‘d’, e.g. “exp(-abs(d))”, or “d<3”, that can be parsed by eval(), that computes the distance dependent distribution.
- allow_self_connections – if the connector is used to connect a Population to itself, this flag determines whether a neuron is allowed to connect to itself, or only to other neurons in the Population.
- space (pyNN.Space) – a Space object, needed if you wish to specify distance-dependent weights or delays.
- n_connections (int or None) – The number of efferent synaptic connections per neuron.
-
allow_self_connections¶
-
create_synaptic_block(weights, delays, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Create a synaptic block from the data.
-
d_expression¶
-
get_delay_maximum(delays)[source]¶ Get the maximum delay specified by the user in ms, or None if unbounded.
-
get_n_connections_from_pre_vertex_maximum(delays, post_vertex_slice, min_delay=None, max_delay=None)[source]¶ Get the maximum number of connections between those from any neuron in the pre vertex to the neurons in the post_vertex_slice, for connections with a delay between min_delay and max_delay (inclusive) if both specified (otherwise all connections).
-
class
spynnaker.pyNN.models.neural_projections.connectors.FixedNumberPostConnector(n, allow_self_connections=True, with_replacement=False, safe=True, verbose=False, rng=None)[source]¶ -
Connects a fixed number of post-synaptic neurons selected at random, to all pre-synaptic neurons.
Parameters: - n (int) – number of random post-synaptic neurons connected to pre-neurons.
- allow_self_connections (bool) – if the connector is used to connect a Population to itself, this flag determines whether a neuron is allowed to connect to itself, or only to other neurons in the Population.
- with_replacement (bool) – this flag determines how the random selection of post-synaptic neurons is performed; if true, then every post-synaptic neuron can be chosen on each occasion, and so multiple connections between neuron pairs are possible; if false, then once a post-synaptic neuron has been connected to a pre-neuron, it can’t be connected again.
-
allow_self_connections¶
-
create_synaptic_block(weights, delays, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Create a synaptic block from the data.
-
gen_connector_id¶ Get the id of the connection generator on the machine
Return type: int
-
gen_connector_params(pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Get the parameters of the on machine generation.
Return type: numpy array of uint32
-
gen_connector_params_size_in_bytes¶ The size of the connector parameters in bytes.
Return type: int
-
get_delay_maximum(delays)[source]¶ Get the maximum delay specified by the user in ms, or None if unbounded.
-
get_n_connections_from_pre_vertex_maximum(delays, post_vertex_slice, min_delay=None, max_delay=None)[source]¶ Get the maximum number of connections between those from any neuron in the pre vertex to the neurons in the post_vertex_slice, for connections with a delay between min_delay and max_delay (inclusive) if both specified (otherwise all connections).
-
class
spynnaker.pyNN.models.neural_projections.connectors.FixedNumberPreConnector(n, allow_self_connections=True, with_replacement=False, safe=True, verbose=False, rng=None)[source]¶ -
Connects a fixed number of pre-synaptic neurons selected at random, to all post-synaptic neurons.
Parameters: - n (int) – number of random pre-synaptic neurons connected to output
- allow_self_connections (bool) – if the connector is used to connect a Population to itself, this flag determines whether a neuron is allowed to connect to itself, or only to other neurons in the Population.
- with_replacement (bool) – this flag determines how the random selection of pre-synaptic neurons is performed; if true, then every pre-synaptic neuron can be chosen on each occasion, and so multiple connections between neuron pairs are possible; if false, then once a pre-synaptic neuron has been connected to a post-neuron, it can’t be connected again.
-
allow_self_connections¶
-
create_synaptic_block(weights, delays, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Create a synaptic block from the data.
-
gen_connector_id¶ Get the id of the connection generator on the machine
Return type: int
-
gen_connector_params(pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Get the parameters of the on machine generation.
Return type: numpy array of uint32
-
gen_connector_params_size_in_bytes¶ The size of the connector parameters in bytes.
Return type: int
-
get_delay_maximum(delays)[source]¶ Get the maximum delay specified by the user in ms, or None if unbounded.
-
get_n_connections_from_pre_vertex_maximum(delays, post_vertex_slice, min_delay=None, max_delay=None)[source]¶ Get the maximum number of connections between those from any neuron in the pre vertex to the neurons in the post_vertex_slice, for connections with a delay between min_delay and max_delay (inclusive) if both specified (otherwise all connections).
-
class
spynnaker.pyNN.models.neural_projections.connectors.FixedProbabilityConnector(p_connect, allow_self_connections=True, safe=True, verbose=False, rng=None)[source]¶ -
For each pair of pre-post cells, the connection probability is constant.
Parameters: - p_connect (float) – a float between zero and one. Each potential connection is created with this probability.
- allow_self_connections (bool) – if the connector is used to connect a Population to itself, this flag determines whether a neuron is allowed to connect to itself, or only to other neurons in the Population.
- space (pyNN.Space) – a Space object, needed if you wish to specify distance-dependent weights or delays - not implemented
-
create_synaptic_block(weights, delays, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Create a synaptic block from the data.
-
gen_connector_id¶ Get the id of the connection generator on the machine
Return type: int
-
gen_connector_params(pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Get the parameters of the on machine generation.
Return type: numpy array of uint32
-
gen_connector_params_size_in_bytes¶ The size of the connector parameters in bytes.
Return type: int
-
get_delay_maximum(delays)[source]¶ Get the maximum delay specified by the user in ms, or None if unbounded.
-
get_n_connections_from_pre_vertex_maximum(delays, post_vertex_slice, min_delay=None, max_delay=None)[source]¶ Get the maximum number of connections between those from any neuron in the pre vertex to the neurons in the post_vertex_slice, for connections with a delay between min_delay and max_delay (inclusive) if both specified (otherwise all connections).
-
class
spynnaker.pyNN.models.neural_projections.connectors.FromListConnector(conn_list, safe=True, verbose=False, column_names=None)[source]¶ Bases:
spynnaker.pyNN.models.neural_projections.connectors.abstract_connector.AbstractConnectorMake connections according to a list.
Param: conn_list: a list of tuples, one tuple for each connection. Each tuple should contain at least:
(pre_idx, post_idx)
where pre_idx is the index (i.e. order in the Population, not the ID) of the presynaptic neuron, and post_idx is the index of the postsynaptic neuron.
Additional items per synapse are acceptable but all synapses should have the same number of items.
-
column_names¶
-
conn_list¶
-
create_synaptic_block(weights, delays, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Create a synaptic block from the data.
-
get_delay_maximum(delays)[source]¶ Get the maximum delay specified by the user in ms, or None if unbounded.
-
get_n_connections_from_pre_vertex_maximum(delays, post_vertex_slice, min_delay=None, max_delay=None)[source]¶ Get the maximum number of connections between those from any neuron in the pre vertex to the neurons in the post_vertex_slice, for connections with a delay between min_delay and max_delay (inclusive) if both specified (otherwise all connections).
-
-
class
spynnaker.pyNN.models.neural_projections.connectors.IndexBasedProbabilityConnector(index_expression, allow_self_connections=True, rng=None, safe=True, callback=None, verbose=False)[source]¶ Bases:
spynnaker.pyNN.models.neural_projections.connectors.abstract_connector.AbstractConnectorMake connections using a probability distribution which varies dependent upon the indices of the pre- and post-populations.
Parameters: - index_expression (string) – the right-hand side of a valid python expression for probability, involving the indices of the pre and post populations, that can be parsed by eval(), that computes a probability dist.
- allow_self_connections (bool) – if the connector is used to connect a Population to itself, this flag determines whether a neuron is allowed to connect to itself, or only to other neurons in the Population.
-
allow_self_connections¶
-
create_synaptic_block(weights, delays, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Create a synaptic block from the data.
-
get_delay_maximum(delays)[source]¶ Get the maximum delay specified by the user in ms, or None if unbounded.
-
get_n_connections_from_pre_vertex_maximum(delays, post_vertex_slice, min_delay=None, max_delay=None)[source]¶ Get the maximum number of connections between those from any neuron in the pre vertex to the neurons in the post_vertex_slice, for connections with a delay between min_delay and max_delay (inclusive) if both specified (otherwise all connections).
-
get_n_connections_to_post_vertex_maximum()[source]¶ Get the maximum number of connections between those to any neuron in the post vertex from neurons in the pre vertex.
-
index_expression¶
-
class
spynnaker.pyNN.models.neural_projections.connectors.MultapseConnector(num_synapses, allow_self_connections=True, with_replacement=True, safe=True, verbose=False, rng=None)[source]¶ -
Create a multapse connector. The size of the source and destination populations are obtained when the projection is connected. The number of synapses is specified. when instantiated, the required number of synapses is created by selecting at random from the source and target populations with replacement. Uniform selection probability is assumed.
Parameters: - num_synapses (int) – This is the total number of synapses in the connection.
- allow_self_connections (bool) – Allow a neuron to connect to itself or not.
- with_replacement (bool) – When selecting, allow a neuron to be re-selected or not.
-
create_synaptic_block(weights, delays, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Create a synaptic block from the data.
-
gen_connector_id¶ Get the id of the connection generator on the machine
Return type: int
-
gen_connector_params(pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Get the parameters of the on machine generation.
Return type: numpy array of uint32
-
gen_connector_params_size_in_bytes¶ The size of the connector parameters in bytes.
Return type: int
-
get_delay_maximum(delays)[source]¶ Get the maximum delay specified by the user in ms, or None if unbounded.
-
get_n_connections_from_pre_vertex_maximum(delays, post_vertex_slice, min_delay=None, max_delay=None)[source]¶ Get the maximum number of connections between those from any neuron in the pre vertex to the neurons in the post_vertex_slice, for connections with a delay between min_delay and max_delay (inclusive) if both specified (otherwise all connections).
-
class
spynnaker.pyNN.models.neural_projections.connectors.OneToOneConnector(random_number_class, safe=True, verbose=False)[source]¶ -
Where the pre- and postsynaptic populations have the same size, connect cell i in the presynaptic pynn_population.py to cell i in the postsynaptic pynn_population.py for all i.
-
create_synaptic_block(weights, delays, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Create a synaptic block from the data.
-
gen_connector_id¶ Get the id of the connection generator on the machine
Return type: int
-
get_delay_maximum(delays)[source]¶ Get the maximum delay specified by the user in ms, or None if unbounded.
-
get_n_connections_from_pre_vertex_maximum(delays, post_vertex_slice, min_delay=None, max_delay=None)[source]¶ Get the maximum number of connections between those from any neuron in the pre vertex to the neurons in the post_vertex_slice, for connections with a delay between min_delay and max_delay (inclusive) if both specified (otherwise all connections).
-
-
class
spynnaker.pyNN.models.neural_projections.connectors.SmallWorldConnector(degree, rewiring, allow_self_connections=True, safe=True, verbose=False, n_connections=None)[source]¶ Bases:
spynnaker.pyNN.models.neural_projections.connectors.abstract_connector.AbstractConnector-
create_synaptic_block(weights, delays, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Create a synaptic block from the data.
-
get_delay_maximum(delays)[source]¶ Get the maximum delay specified by the user in ms, or None if unbounded.
-
get_n_connections_from_pre_vertex_maximum(delays, post_vertex_slice, min_delay=None, max_delay=None)[source]¶ Get the maximum number of connections between those from any neuron in the pre vertex to the neurons in the post_vertex_slice, for connections with a delay between min_delay and max_delay (inclusive) if both specified (otherwise all connections).
-
-
class
spynnaker.pyNN.models.neural_projections.connectors.KernelConnector(shape_pre, shape_post, shape_kernel, weight_kernel, delay_kernel, shape_common, pre_sample_steps, pre_start_coords, post_sample_steps, post_start_coords, safe, space, verbose)[source]¶ -
Where the pre- and post-synaptic populations are considered as a 2D array. Connect every post(row, col) neuron to many pre(row, col, kernel) through a (kernel) set of weights and/or delays.
TODO: should these include allow_self_connections and with_replacement?
Parameters: - shape_pre – 2D shape of the pre population (rows/height, cols/width, usually the input image shape)
- shape_post – 2D shape of the post population (rows/height, cols/width)
- shape_kernel – 2D shape of the kernel (rows/height, cols/width)
- (optional) (pre/post_start_coords) – 2D matrix of size shape_kernel describing the weights
- (optional) – 2D matrix of size shape_kernel describing the delays
- (optional) – 2D shape of common coordinate system (for both pre and post, usually the input image sizes)
- (optional) – Sampling steps/jumps for pre/post pop <=> (startX, endX, _stepX_) None or 2-item array
- (optional) – Starting row/col for pre/post sampling <=> (_startX_, endX, stepX) None or 2-item array
-
create_synaptic_block(weights, delays, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Create a synaptic block from the data.
-
gen_connector_id¶ Get the id of the connection generator on the machine
Return type: int
-
gen_connector_params(pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_type)[source]¶ Get the parameters of the on machine generation.
Return type: numpy array of uint32
-
gen_connector_params_size_in_bytes¶ The size of the connector parameters in bytes.
Return type: int
-
gen_delay_params(delays, pre_vertex_slice, post_vertex_slice)[source]¶ Get the parameters of the delay generator on the machine
Return type: numpy array of uint32
-
gen_delay_params_size_in_bytes(delays)[source]¶ The size of the delay parameters in bytes
Return type: int
-
gen_weight_params_size_in_bytes(weights)[source]¶ The size of the weight parameters in bytes
Return type: int
-
gen_weights_params(weights, pre_vertex_slice, post_vertex_slice)[source]¶ Get the parameters of the weight generator on the machine
Return type: numpy array of uint32
-
generate_on_machine¶ Determine if this instance can generate on the machine.
Default implementation returns True if the weights and delays can be generated on the machine
Return type: bool
-
get_delay_maximum(delays)[source]¶ Get the maximum delay specified by the user in ms, or None if unbounded.
-
get_n_connections_from_pre_vertex_maximum(delays, post_vertex_slice, min_delay=None, max_delay=None)[source]¶ Get the maximum number of connections between those from any neuron in the pre vertex to the neurons in the post_vertex_slice, for connections with a delay between min_delay and max_delay (inclusive) if both specified (otherwise all connections).
-
class
spynnaker.pyNN.models.neural_projections.delay_afferent_application_edge.DelayAfferentApplicationEdge(prevertex, delayvertex, label=None)[source]¶ Bases:
pacman.model.graphs.application.application_edge.ApplicationEdge-
create_machine_edge(pre_vertex, post_vertex, label)[source]¶ Create a machine edge between two machine vertices
Parameters: - pre_vertex (
pacman.model.graphs.machine.MachineVertex) – The machine vertex at the start of the edge - post_vertex (
pacman.model.graphs.machine.MachineVertex) – The machine vertex at the end of the edge - label (str) – label of the edge
Returns: The created machine edge
Return type: - pre_vertex (
-
-
class
spynnaker.pyNN.models.neural_projections.delay_afferent_machine_edge.DelayAfferentMachineEdge(pre_vertex, post_vertex, label, weight=1)[source]¶ Bases:
pacman.model.graphs.machine.machine_edge.MachineEdge,spynnaker.pyNN.models.abstract_models.abstract_filterable_edge.AbstractFilterableEdge,spynnaker.pyNN.models.abstract_models.abstract_weight_updatable.AbstractWeightUpdatable
-
class
spynnaker.pyNN.models.neural_projections.delayed_application_edge.DelayedApplicationEdge(pre_vertex, post_vertex, synapse_information, label=None)[source]¶ Bases:
pacman.model.graphs.application.application_edge.ApplicationEdge-
create_machine_edge(pre_vertex, post_vertex, label)[source]¶ Create a machine edge between two machine vertices
Parameters: - pre_vertex (
pacman.model.graphs.machine.MachineVertex) – The machine vertex at the start of the edge - post_vertex (
pacman.model.graphs.machine.MachineVertex) – The machine vertex at the end of the edge - label (str) – label of the edge
Returns: The created machine edge
Return type: - pre_vertex (
-
synapse_information¶
-
-
class
spynnaker.pyNN.models.neural_projections.delayed_machine_edge.DelayedMachineEdge(synapse_information, pre_vertex, post_vertex, label=None, weight=1)[source]¶ Bases:
pacman.model.graphs.machine.machine_edge.MachineEdge,spynnaker.pyNN.models.abstract_models.abstract_filterable_edge.AbstractFilterableEdge
-
class
spynnaker.pyNN.models.neural_projections.projection_application_edge.ProjectionApplicationEdge(pre_vertex, post_vertex, synapse_information, label=None)[source]¶ Bases:
pacman.model.graphs.application.application_edge.ApplicationEdgeAn edge which terminates on an
AbstractPopulationVertex.-
create_machine_edge(pre_vertex, post_vertex, label)[source]¶ Create a machine edge between two machine vertices
Parameters: - pre_vertex (
pacman.model.graphs.machine.MachineVertex) – The machine vertex at the start of the edge - post_vertex (
pacman.model.graphs.machine.MachineVertex) – The machine vertex at the end of the edge - label (str) – label of the edge
Returns: The created machine edge
Return type: - pre_vertex (
-
delay_edge¶
-
n_delay_stages¶
-
synapse_information¶
-
-
class
spynnaker.pyNN.models.neural_projections.projection_machine_edge.ProjectionMachineEdge(synapse_information, pre_vertex, post_vertex, label=None, traffic_weight=1)[source]¶ Bases:
pacman.model.graphs.machine.machine_edge.MachineEdge,spynnaker.pyNN.models.abstract_models.abstract_filterable_edge.AbstractFilterableEdge,spynnaker.pyNN.models.abstract_models.abstract_weight_updatable.AbstractWeightUpdatable,spinn_front_end_common.interface.provenance.abstract_provides_local_provenance_data.AbstractProvidesLocalProvenanceData-
filter_edge(graph_mapper)[source]¶ Determine if this edge should be filtered out
Parameters: graph_mapper – the mapper between graphs Returns: True if the edge should be filtered Return type: bool
-
get_local_provenance_data()[source]¶ Get an iterable of provenance data items
Returns: iterable of ProvenanceDataItem
-
synapse_information¶
-
-
class
spynnaker.pyNN.models.neural_projections.synapse_information.SynapseInformation(connector, synapse_dynamics, synapse_type, weight=None, delay=None)[source]¶ Bases:
objectContains the synapse information including the connector, synapse type and synapse dynamics
-
connector¶
-
delay¶
-
synapse_dynamics¶
-
synapse_type¶
-
weight¶
-
-
class
spynnaker.pyNN.models.neural_projections.DelayAfferentApplicationEdge(prevertex, delayvertex, label=None)[source]¶ Bases:
pacman.model.graphs.application.application_edge.ApplicationEdge-
create_machine_edge(pre_vertex, post_vertex, label)[source]¶ Create a machine edge between two machine vertices
Parameters: - pre_vertex (
pacman.model.graphs.machine.MachineVertex) – The machine vertex at the start of the edge - post_vertex (
pacman.model.graphs.machine.MachineVertex) – The machine vertex at the end of the edge - label (str) – label of the edge
Returns: The created machine edge
Return type: - pre_vertex (
-
-
class
spynnaker.pyNN.models.neural_projections.DelayAfferentMachineEdge(pre_vertex, post_vertex, label, weight=1)[source]¶ Bases:
pacman.model.graphs.machine.machine_edge.MachineEdge,spynnaker.pyNN.models.abstract_models.abstract_filterable_edge.AbstractFilterableEdge,spynnaker.pyNN.models.abstract_models.abstract_weight_updatable.AbstractWeightUpdatable
-
class
spynnaker.pyNN.models.neural_projections.DelayedApplicationEdge(pre_vertex, post_vertex, synapse_information, label=None)[source]¶ Bases:
pacman.model.graphs.application.application_edge.ApplicationEdge-
create_machine_edge(pre_vertex, post_vertex, label)[source]¶ Create a machine edge between two machine vertices
Parameters: - pre_vertex (
pacman.model.graphs.machine.MachineVertex) – The machine vertex at the start of the edge - post_vertex (
pacman.model.graphs.machine.MachineVertex) – The machine vertex at the end of the edge - label (str) – label of the edge
Returns: The created machine edge
Return type: - pre_vertex (
-
synapse_information¶
-
-
class
spynnaker.pyNN.models.neural_projections.DelayedMachineEdge(synapse_information, pre_vertex, post_vertex, label=None, weight=1)[source]¶ Bases:
pacman.model.graphs.machine.machine_edge.MachineEdge,spynnaker.pyNN.models.abstract_models.abstract_filterable_edge.AbstractFilterableEdge
-
class
spynnaker.pyNN.models.neural_projections.ProjectionApplicationEdge(pre_vertex, post_vertex, synapse_information, label=None)[source]¶ Bases:
pacman.model.graphs.application.application_edge.ApplicationEdgeAn edge which terminates on an
AbstractPopulationVertex.-
create_machine_edge(pre_vertex, post_vertex, label)[source]¶ Create a machine edge between two machine vertices
Parameters: - pre_vertex (
pacman.model.graphs.machine.MachineVertex) – The machine vertex at the start of the edge - post_vertex (
pacman.model.graphs.machine.MachineVertex) – The machine vertex at the end of the edge - label (str) – label of the edge
Returns: The created machine edge
Return type: - pre_vertex (
-
delay_edge¶
-
n_delay_stages¶
-
synapse_information¶
-
-
class
spynnaker.pyNN.models.neural_projections.ProjectionMachineEdge(synapse_information, pre_vertex, post_vertex, label=None, traffic_weight=1)[source]¶ Bases:
pacman.model.graphs.machine.machine_edge.MachineEdge,spynnaker.pyNN.models.abstract_models.abstract_filterable_edge.AbstractFilterableEdge,spynnaker.pyNN.models.abstract_models.abstract_weight_updatable.AbstractWeightUpdatable,spinn_front_end_common.interface.provenance.abstract_provides_local_provenance_data.AbstractProvidesLocalProvenanceData-
filter_edge(graph_mapper)[source]¶ Determine if this edge should be filtered out
Parameters: graph_mapper – the mapper between graphs Returns: True if the edge should be filtered Return type: bool
-
get_local_provenance_data()[source]¶ Get an iterable of provenance data items
Returns: iterable of ProvenanceDataItem
-
synapse_information¶
-
-
class
spynnaker.pyNN.models.neural_projections.SynapseInformation(connector, synapse_dynamics, synapse_type, weight=None, delay=None)[source]¶ Bases:
objectContains the synapse information including the connector, synapse type and synapse dynamics
-
connector¶
-
delay¶
-
synapse_dynamics¶
-
synapse_type¶
-
weight¶
-
-
class
spynnaker.pyNN.models.neuron.additional_inputs.additional_input_ca2_adaptive.AdditionalInputCa2Adaptive(tau_ca2, i_ca2, i_alpha)[source]¶ Bases:
spynnaker.pyNN.models.neuron.additional_inputs.abstract_additional_input.AbstractAdditionalInput-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
get_values(parameters, state_variables, vertex_slice, ts)[source]¶ Get the values to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of variables being retrieved
Returns: A list with the same length as self.struct.field_types
Return type: A list of (single value or list of values or RangedList)
- parameters (
-
has_variable(variable)[source]¶ Determine if this component has a variable by the given name
Parameters: variable (str) – The name of the variable Return type: bool
-
i_alpha¶
-
i_ca2¶
-
tau_ca2¶
-
update_values(values, parameters, state_variables)[source]¶ Update the parameters and state variables with the given struct values that have been read from the machine
Parameters: - values – The values read from the machine, one for each struct element
- parameters – The holder of the parameters to update
- state_variables – The holder of the state variables to update
-
-
class
spynnaker.pyNN.models.neuron.additional_inputs.AbstractAdditionalInput(data_types)[source]¶ -
Represents a possible additional independent input for a model.
Parameters: data_types – A list of data types in the component structure, in the order that they appear
-
class
spynnaker.pyNN.models.neuron.additional_inputs.AdditionalInputCa2Adaptive(tau_ca2, i_ca2, i_alpha)[source]¶ Bases:
spynnaker.pyNN.models.neuron.additional_inputs.abstract_additional_input.AbstractAdditionalInput-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
get_values(parameters, state_variables, vertex_slice, ts)[source]¶ Get the values to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of variables being retrieved
Returns: A list with the same length as self.struct.field_types
Return type: A list of (single value or list of values or RangedList)
- parameters (
-
has_variable(variable)[source]¶ Determine if this component has a variable by the given name
Parameters: variable (str) – The name of the variable Return type: bool
-
i_alpha¶
-
i_ca2¶
-
tau_ca2¶
-
update_values(values, parameters, state_variables)[source]¶ Update the parameters and state variables with the given struct values that have been read from the machine
Parameters: - values – The values read from the machine, one for each struct element
- parameters – The holder of the parameters to update
- state_variables – The holder of the state variables to update
-
-
class
spynnaker.pyNN.models.neuron.builds.eif_cond_alpha_isfa_ista.EIFConductanceAlphaPopulation(**kwargs)[source]¶ Bases:
objectExponential integrate and fire neuron with spike triggered and sub-threshold adaptation currents (isfa, ista reps.)
-
default_initial_values= {'gsyn_exc': 0.0, 'gsyn_inh': 0.0, 'v': -70.6, 'w': 0.0}¶
-
default_parameters= {'a': 4.0, 'b': 0.0805, 'cm': 0.281, 'delta_T': 2.0, 'e_rev_E': 0.0, 'e_rev_I': -80.0, 'i_offset': 0.0, 'tau_m': 9.3667, 'tau_refrac': 0.1, 'tau_syn_E': 5.0, 'tau_syn_I': 0.5, 'tau_w': 144.0, 'v_reset': -70.6, 'v_rest': -70.6, 'v_spike': -40.0, 'v_thresh': -50.4}¶
-
-
class
spynnaker.pyNN.models.neuron.builds.hh_cond_exp.HHCondExp(**kwargs)[source]¶ Bases:
objectSingle-compartment Hodgkin-Huxley model with exponentially decaying current input.
-
default_initial_values= {'gsyn_exc': 0.0, 'gsyn_inh': 0.0, 'v': -65.0}¶
-
default_parameters= {'cm': 0.2, 'e_rev_E': 0.0, 'e_rev_I': -80, 'e_rev_K': -90.0, 'e_rev_Na': 50.0, 'e_rev_leak': -65.0, 'g_leak': 0.01, 'gbar_K': 6.0, 'gbar_Na': 20.0, 'i_offset': 0.0, 'tau_syn_E': 0.2, 'tau_syn_I': 2.0, 'v_offset': -63}¶
-
-
class
spynnaker.pyNN.models.neuron.builds.if_cond_alpha.IFCondAlpha(**kwargs)[source]¶ Bases:
objectLeaky integrate and fire neuron with an alpha-shaped current input.
-
default_initial_values= {'gsyn_exc': 0.0, 'gsyn_inh': 0.0, 'v': -65.0}¶
-
default_parameters= {'cm': 1.0, 'e_rev_E': 0.0, 'e_rev_I': -70.0, 'i_offset': 0, 'tau_m': 20, 'tau_refrac': 0.1, 'tau_syn_E': 0.3, 'tau_syn_I': 0.5, 'v_reset': -65.0, 'v_rest': -65.0, 'v_thresh': -50.0}¶
-
-
class
spynnaker.pyNN.models.neuron.builds.if_cond_exp_base.IFCondExpBase(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandardLeaky integrate and fire neuron with an exponentially decaying conductance input.
-
class
spynnaker.pyNN.models.neuron.builds.if_cond_exp_stoc.IFCondExpStoc(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandardLeaky integrate and fire neuron with a stochastic threshold.
-
class
spynnaker.pyNN.models.neuron.builds.if_curr_alpha.IFCurrAlpha(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandardLeaky integrate and fire neuron with an alpha-shaped current-based input.
-
class
spynnaker.pyNN.models.neuron.builds.if_curr_delta.IFCurrDelta(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandardLeaky integrate and fire neuron with an instantaneous current input
-
class
spynnaker.pyNN.models.neuron.builds.if_curr_dual_exp_base.IFCurrDualExpBase(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandardLeaky integrate and fire neuron with two exponentially decaying excitatory current inputs, and one exponentially decaying inhibitory current input
-
class
spynnaker.pyNN.models.neuron.builds.if_curr_exp_base.IFCurrExpBase(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandardLeaky integrate and fire neuron with an exponentially decaying current input
-
class
spynnaker.pyNN.models.neuron.builds.if_curr_exp_ca2_adaptive.IFCurrExpCa2Adaptive(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandardModel from Liu, Y. H., & Wang, X. J. (2001). Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron. Journal of Computational Neuroscience, 10(1), 25-45. doi:10.1023/A:1008916026143
-
class
spynnaker.pyNN.models.neuron.builds.if_curr_exp_semd_base.IFCurrExpSEMDBase(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandardLeaky integrate and fire neuron with an exponentially decaying current input, where the excitatory input depends upon the inhibitory input (see https://www.cit-ec.de/en/nbs/spiking-insect-vision)
-
class
spynnaker.pyNN.models.neuron.builds.if_facets_hardware1.IFFacetsConductancePopulation(**kwargs)[source]¶ Bases:
objectLeaky integrate and fire neuron with conductance-based synapses and fixed threshold as it is resembled by the FACETS Hardware Stage 1
-
default_initial_values= {'v': -65.0}¶
-
default_parameters= {'e_rev_I': -80, 'g_leak': 40.0, 'tau_syn_E': 30.0, 'tau_syn_I': 30.0, 'v_reset': -80.0, 'v_rest': -65.0, 'v_thresh': -55.0}¶
-
-
class
spynnaker.pyNN.models.neuron.builds.izk_cond_exp_base.IzkCondExpBase(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard
-
class
spynnaker.pyNN.models.neuron.builds.izk_curr_exp_base.IzkCurrExpBase(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard
-
class
spynnaker.pyNN.models.neuron.builds.EIFConductanceAlphaPopulation(**kwargs)[source]¶ Bases:
objectExponential integrate and fire neuron with spike triggered and sub-threshold adaptation currents (isfa, ista reps.)
-
default_initial_values= {'gsyn_exc': 0.0, 'gsyn_inh': 0.0, 'v': -70.6, 'w': 0.0}¶
-
default_parameters= {'a': 4.0, 'b': 0.0805, 'cm': 0.281, 'delta_T': 2.0, 'e_rev_E': 0.0, 'e_rev_I': -80.0, 'i_offset': 0.0, 'tau_m': 9.3667, 'tau_refrac': 0.1, 'tau_syn_E': 5.0, 'tau_syn_I': 0.5, 'tau_w': 144.0, 'v_reset': -70.6, 'v_rest': -70.6, 'v_spike': -40.0, 'v_thresh': -50.4}¶
-
-
class
spynnaker.pyNN.models.neuron.builds.HHCondExp(**kwargs)[source]¶ Bases:
objectSingle-compartment Hodgkin-Huxley model with exponentially decaying current input.
-
default_initial_values= {'gsyn_exc': 0.0, 'gsyn_inh': 0.0, 'v': -65.0}¶
-
default_parameters= {'cm': 0.2, 'e_rev_E': 0.0, 'e_rev_I': -80, 'e_rev_K': -90.0, 'e_rev_Na': 50.0, 'e_rev_leak': -65.0, 'g_leak': 0.01, 'gbar_K': 6.0, 'gbar_Na': 20.0, 'i_offset': 0.0, 'tau_syn_E': 0.2, 'tau_syn_I': 2.0, 'v_offset': -63}¶
-
-
class
spynnaker.pyNN.models.neuron.builds.IFCondAlpha(**kwargs)[source]¶ Bases:
objectLeaky integrate and fire neuron with an alpha-shaped current input.
-
default_initial_values= {'gsyn_exc': 0.0, 'gsyn_inh': 0.0, 'v': -65.0}¶
-
default_parameters= {'cm': 1.0, 'e_rev_E': 0.0, 'e_rev_I': -70.0, 'i_offset': 0, 'tau_m': 20, 'tau_refrac': 0.1, 'tau_syn_E': 0.3, 'tau_syn_I': 0.5, 'v_reset': -65.0, 'v_rest': -65.0, 'v_thresh': -50.0}¶
-
-
class
spynnaker.pyNN.models.neuron.builds.IFCondExpBase(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandardLeaky integrate and fire neuron with an exponentially decaying conductance input.
-
class
spynnaker.pyNN.models.neuron.builds.IFCurrAlpha(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandardLeaky integrate and fire neuron with an alpha-shaped current-based input.
-
class
spynnaker.pyNN.models.neuron.builds.IFCurrDualExpBase(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandardLeaky integrate and fire neuron with two exponentially decaying excitatory current inputs, and one exponentially decaying inhibitory current input
-
class
spynnaker.pyNN.models.neuron.builds.IFCurrExpBase(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandardLeaky integrate and fire neuron with an exponentially decaying current input
-
class
spynnaker.pyNN.models.neuron.builds.IFFacetsConductancePopulation(**kwargs)[source]¶ Bases:
objectLeaky integrate and fire neuron with conductance-based synapses and fixed threshold as it is resembled by the FACETS Hardware Stage 1
-
default_initial_values= {'v': -65.0}¶
-
default_parameters= {'e_rev_I': -80, 'g_leak': 40.0, 'tau_syn_E': 30.0, 'tau_syn_I': 30.0, 'v_reset': -80.0, 'v_rest': -65.0, 'v_thresh': -55.0}¶
-
-
class
spynnaker.pyNN.models.neuron.builds.IzkCondExpBase(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard
-
class
spynnaker.pyNN.models.neuron.builds.IzkCurrExpBase(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard
-
class
spynnaker.pyNN.models.neuron.builds.IFCondExpStoc(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandardLeaky integrate and fire neuron with a stochastic threshold.
-
class
spynnaker.pyNN.models.neuron.builds.IFCurrDelta(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandardLeaky integrate and fire neuron with an instantaneous current input
-
class
spynnaker.pyNN.models.neuron.builds.IFCurrExpCa2Adaptive(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandardModel from Liu, Y. H., & Wang, X. J. (2001). Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron. Journal of Computational Neuroscience, 10(1), 25-45. doi:10.1023/A:1008916026143
-
class
spynnaker.pyNN.models.neuron.builds.IFCurrExpSEMDBase(**kwargs)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandardLeaky integrate and fire neuron with an exponentially decaying current input, where the excitatory input depends upon the inhibitory input (see https://www.cit-ec.de/en/nbs/spiking-insect-vision)
-
class
spynnaker.pyNN.models.neuron.implementations.abstract_neuron_impl.AbstractNeuronImpl[source]¶ Bases:
objectAn abstraction of a whole neuron model including all parts
-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
binary_name¶ The name of the binary executable of this implementation
:rtype str
-
get_data(parameters, state_variables, vertex_slice)[source]¶ Get the data to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of the vertex to generate parameters for
Return type: numpy array of uint32
- parameters (
-
get_dtcm_usage_in_bytes(n_neurons)[source]¶ Get the DTCM memory usage required
Parameters: n_neurons (int) – The number of neurons to get the usage for Return type: int
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_n_synapse_types()[source]¶ Get the number of synapse types supported by the model
Return type: int
-
get_recordable_units(variable)[source]¶ Get the units of the given variable that can be recorded
Parameters: variable (str) – The name of the variable
-
get_recordable_variable_index(variable)[source]¶ Get the index of the variable in the list of variables that can be recorded
Parameters: variable (str) – The name of the variable Return type: int
-
get_recordable_variables()[source]¶ Get the names of the variables that can be recorded in this model
Return type: list of str
-
get_sdram_usage_in_bytes(n_neurons)[source]¶ Get the SDRAM memory usage required
Parameters: n_neurons (int) – The number of neurons to get the usage for Return type: int
-
get_synapse_id_by_target(target)[source]¶ Get the id of a synapse given the name
Parameters: target (str) – The name of the synapse Return type: int
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
is_conductance_based¶ Determine if the model uses conductance
Return type: bool
-
is_recordable(variable)[source]¶ Determine if the given variable can be recorded
Parameters: variable (str) – The name of the variable being requested Return type: bool
-
model_name¶ The name of the model
Return type: str
-
read_data(data, offset, vertex_slice, parameters, state_variables)[source]¶ Read the parameters and state variables of the model from the given data
Parameters: - data – The data to be read
- offset – The offset where the data should be read from
- vertex_slice – The slice of the vertex to read parameters for
- parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters to update - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables to update
-
-
class
spynnaker.pyNN.models.neuron.implementations.abstract_standard_neuron_component.AbstractStandardNeuronComponent(data_types)[source]¶ Bases:
objectRepresents a component of a standard neural model
Parameters: data_types – A list of data types in the component structure, in the order that they appear -
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
get_data(parameters, state_variables, vertex_slice)[source]¶ Get the data to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of the vertex to generate parameters for
Return type: numpy array of uint32
- parameters (
-
get_dtcm_usage_in_bytes(n_neurons)[source]¶ Get the DTCM memory usage required
Parameters: n_neurons (int) – The number of neurons to get the usage for Return type: int
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_sdram_usage_in_bytes(n_neurons)[source]¶ Get the SDRAM memory usage required
Parameters: n_neurons (int) – The number of neurons to get the usage for Return type: int
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
get_values(parameters, state_variables, vertex_slice)[source]¶ Get the values to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of variables being retrieved
Returns: A list with the same length as self.struct.field_types
Return type: A list of (single value or list of values or RangedList)
- parameters (
-
has_variable(variable)[source]¶ Determine if this component has a variable by the given name
Parameters: variable (str) – The name of the variable Return type: bool
-
read_data(data, offset, vertex_slice, parameters, state_variables)[source]¶ Read the parameters and state variables of the model from the given data
Parameters: - data – The data to be read
- offset – The offset where the data should be read from
- vertex_slice – The slice of the vertex to read parameters for
- parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters to update - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables to update
Returns: The offset after reading the data
-
struct¶ The structure of the component
Return type: :py:class:’spynnaker.pyNN.models.neuron.implementations.struct.Struct’
-
update_values(values, parameters, state_variables)[source]¶ Update the parameters and state variables with the given struct values that have been read from the machine
Parameters: - values – The values read from the machine, one for each struct element
- parameters – The holder of the parameters to update
- state_variables – The holder of the state variables to update
-
-
class
spynnaker.pyNN.models.neuron.implementations.neuron_impl_standard.NeuronImplStandard(model_name, binary, neuron_model, input_type, synapse_type, threshold_type, additional_input_type=None)[source]¶ Bases:
spynnaker.pyNN.models.neuron.implementations.abstract_neuron_impl.AbstractNeuronImplThe standard neuron implementation, consisting of various components
-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
binary_name¶ The name of the binary executable of this implementation
:rtype str
-
get_data(parameters, state_variables, vertex_slice)[source]¶ Get the data to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of the vertex to generate parameters for
Return type: numpy array of uint32
- parameters (
-
get_dtcm_usage_in_bytes(n_neurons)[source]¶ Get the DTCM memory usage required
Parameters: n_neurons (int) – The number of neurons to get the usage for Return type: int
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_n_synapse_types()[source]¶ Get the number of synapse types supported by the model
Return type: int
-
get_recordable_units(variable)[source]¶ Get the units of the given variable that can be recorded
Parameters: variable (str) – The name of the variable
-
get_recordable_variable_index(variable)[source]¶ Get the index of the variable in the list of variables that can be recorded
Parameters: variable (str) – The name of the variable Return type: int
-
get_recordable_variables()[source]¶ Get the names of the variables that can be recorded in this model
Return type: list of str
-
get_sdram_usage_in_bytes(n_neurons)[source]¶ Get the SDRAM memory usage required
Parameters: n_neurons (int) – The number of neurons to get the usage for Return type: int
-
get_synapse_id_by_target(target)[source]¶ Get the id of a synapse given the name
Parameters: target (str) – The name of the synapse Return type: int
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
is_conductance_based¶ Determine if the model uses conductance
Return type: bool
-
is_recordable(variable)[source]¶ Determine if the given variable can be recorded
Parameters: variable (str) – The name of the variable being requested Return type: bool
-
model_name¶ The name of the model
Return type: str
-
read_data(data, offset, vertex_slice, parameters, state_variables)[source]¶ Read the parameters and state variables of the model from the given data
Parameters: - data – The data to be read
- offset – The offset where the data should be read from
- vertex_slice – The slice of the vertex to read parameters for
- parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters to update - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables to update
-
-
class
spynnaker.pyNN.models.neuron.implementations.struct.Struct(field_types)[source]¶ Bases:
objectRepresents a C code structure
Parameters: field_types (list of data_specification.enums.data_type.DataType) – The types of the fields, ordered as they appear in the struct-
field_types¶ The types of the fields, ordered as they appear in the struct
Return type: list of data_specification.enums.data_type.DataType
-
get_data(values, offset=0, array_size=1)[source]¶ Get a numpy array of uint32 of data for the given values
Parameters: - values (list of (single value or list of values or RangedList of values)) – A list of values with length the same size as the number of fields returned by field_types
- offset – The offset into each of the values where to start
- array_size – The number of structs to generate
Return type: numpy.array(dtype=”uint32”)
-
get_size_in_whole_words(array_size=1)[source]¶ Get the size of the struct in whole words in an array of given size (default 1 item)
Parameters: array_size – The number of elements in an array of structs Return type: int
-
numpy_dtype¶ The numpy data type of the struct
Return type: numpy.dtype
-
read_data(data, offset=0, array_size=1)[source]¶ Read a bytearray of data and convert to struct values
Parameters: - data – The data to be read
- offset – Index of the byte at the start of the valid data
- array_size – The number of struct elements to read
Returns: a list of lists of data values, one list for each struct element
-
-
class
spynnaker.pyNN.models.neuron.implementations.AbstractStandardNeuronComponent(data_types)[source]¶ Bases:
objectRepresents a component of a standard neural model
Parameters: data_types – A list of data types in the component structure, in the order that they appear -
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
get_data(parameters, state_variables, vertex_slice)[source]¶ Get the data to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of the vertex to generate parameters for
Return type: numpy array of uint32
- parameters (
-
get_dtcm_usage_in_bytes(n_neurons)[source]¶ Get the DTCM memory usage required
Parameters: n_neurons (int) – The number of neurons to get the usage for Return type: int
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_sdram_usage_in_bytes(n_neurons)[source]¶ Get the SDRAM memory usage required
Parameters: n_neurons (int) – The number of neurons to get the usage for Return type: int
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
get_values(parameters, state_variables, vertex_slice)[source]¶ Get the values to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of variables being retrieved
Returns: A list with the same length as self.struct.field_types
Return type: A list of (single value or list of values or RangedList)
- parameters (
-
has_variable(variable)[source]¶ Determine if this component has a variable by the given name
Parameters: variable (str) – The name of the variable Return type: bool
-
read_data(data, offset, vertex_slice, parameters, state_variables)[source]¶ Read the parameters and state variables of the model from the given data
Parameters: - data – The data to be read
- offset – The offset where the data should be read from
- vertex_slice – The slice of the vertex to read parameters for
- parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters to update - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables to update
Returns: The offset after reading the data
-
struct¶ The structure of the component
Return type: :py:class:’spynnaker.pyNN.models.neuron.implementations.struct.Struct’
-
update_values(values, parameters, state_variables)[source]¶ Update the parameters and state variables with the given struct values that have been read from the machine
Parameters: - values – The values read from the machine, one for each struct element
- parameters – The holder of the parameters to update
- state_variables – The holder of the state variables to update
-
-
class
spynnaker.pyNN.models.neuron.implementations.Struct(field_types)[source]¶ Bases:
objectRepresents a C code structure
Parameters: field_types (list of data_specification.enums.data_type.DataType) – The types of the fields, ordered as they appear in the struct-
field_types¶ The types of the fields, ordered as they appear in the struct
Return type: list of data_specification.enums.data_type.DataType
-
get_data(values, offset=0, array_size=1)[source]¶ Get a numpy array of uint32 of data for the given values
Parameters: - values (list of (single value or list of values or RangedList of values)) – A list of values with length the same size as the number of fields returned by field_types
- offset – The offset into each of the values where to start
- array_size – The number of structs to generate
Return type: numpy.array(dtype=”uint32”)
-
get_size_in_whole_words(array_size=1)[source]¶ Get the size of the struct in whole words in an array of given size (default 1 item)
Parameters: array_size – The number of elements in an array of structs Return type: int
-
numpy_dtype¶ The numpy data type of the struct
Return type: numpy.dtype
-
read_data(data, offset=0, array_size=1)[source]¶ Read a bytearray of data and convert to struct values
Parameters: - data – The data to be read
- offset – Index of the byte at the start of the valid data
- array_size – The number of struct elements to read
Returns: a list of lists of data values, one list for each struct element
-
-
class
spynnaker.pyNN.models.neuron.implementations.NeuronImplStandard(model_name, binary, neuron_model, input_type, synapse_type, threshold_type, additional_input_type=None)[source]¶ Bases:
spynnaker.pyNN.models.neuron.implementations.abstract_neuron_impl.AbstractNeuronImplThe standard neuron implementation, consisting of various components
-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
binary_name¶ The name of the binary executable of this implementation
:rtype str
-
get_data(parameters, state_variables, vertex_slice)[source]¶ Get the data to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of the vertex to generate parameters for
Return type: numpy array of uint32
- parameters (
-
get_dtcm_usage_in_bytes(n_neurons)[source]¶ Get the DTCM memory usage required
Parameters: n_neurons (int) – The number of neurons to get the usage for Return type: int
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_n_synapse_types()[source]¶ Get the number of synapse types supported by the model
Return type: int
-
get_recordable_units(variable)[source]¶ Get the units of the given variable that can be recorded
Parameters: variable (str) – The name of the variable
-
get_recordable_variable_index(variable)[source]¶ Get the index of the variable in the list of variables that can be recorded
Parameters: variable (str) – The name of the variable Return type: int
-
get_recordable_variables()[source]¶ Get the names of the variables that can be recorded in this model
Return type: list of str
-
get_sdram_usage_in_bytes(n_neurons)[source]¶ Get the SDRAM memory usage required
Parameters: n_neurons (int) – The number of neurons to get the usage for Return type: int
-
get_synapse_id_by_target(target)[source]¶ Get the id of a synapse given the name
Parameters: target (str) – The name of the synapse Return type: int
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
is_conductance_based¶ Determine if the model uses conductance
Return type: bool
-
is_recordable(variable)[source]¶ Determine if the given variable can be recorded
Parameters: variable (str) – The name of the variable being requested Return type: bool
-
model_name¶ The name of the model
Return type: str
-
read_data(data, offset, vertex_slice, parameters, state_variables)[source]¶ Read the parameters and state variables of the model from the given data
Parameters: - data – The data to be read
- offset – The offset where the data should be read from
- vertex_slice – The slice of the vertex to read parameters for
- parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters to update - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables to update
-
-
class
spynnaker.pyNN.models.neuron.implementations.AbstractNeuronImpl[source]¶ Bases:
objectAn abstraction of a whole neuron model including all parts
-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
binary_name¶ The name of the binary executable of this implementation
:rtype str
-
get_data(parameters, state_variables, vertex_slice)[source]¶ Get the data to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of the vertex to generate parameters for
Return type: numpy array of uint32
- parameters (
-
get_dtcm_usage_in_bytes(n_neurons)[source]¶ Get the DTCM memory usage required
Parameters: n_neurons (int) – The number of neurons to get the usage for Return type: int
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_n_synapse_types()[source]¶ Get the number of synapse types supported by the model
Return type: int
-
get_recordable_units(variable)[source]¶ Get the units of the given variable that can be recorded
Parameters: variable (str) – The name of the variable
-
get_recordable_variable_index(variable)[source]¶ Get the index of the variable in the list of variables that can be recorded
Parameters: variable (str) – The name of the variable Return type: int
-
get_recordable_variables()[source]¶ Get the names of the variables that can be recorded in this model
Return type: list of str
-
get_sdram_usage_in_bytes(n_neurons)[source]¶ Get the SDRAM memory usage required
Parameters: n_neurons (int) – The number of neurons to get the usage for Return type: int
-
get_synapse_id_by_target(target)[source]¶ Get the id of a synapse given the name
Parameters: target (str) – The name of the synapse Return type: int
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
is_conductance_based¶ Determine if the model uses conductance
Return type: bool
-
is_recordable(variable)[source]¶ Determine if the given variable can be recorded
Parameters: variable (str) – The name of the variable being requested Return type: bool
-
model_name¶ The name of the model
Return type: str
-
read_data(data, offset, vertex_slice, parameters, state_variables)[source]¶ Read the parameters and state variables of the model from the given data
Parameters: - data – The data to be read
- offset – The offset where the data should be read from
- vertex_slice – The slice of the vertex to read parameters for
- parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters to update - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables to update
-
-
class
spynnaker.pyNN.models.neuron.input_types.input_type_conductance.InputTypeConductance(e_rev_E, e_rev_I)[source]¶ Bases:
spynnaker.pyNN.models.neuron.input_types.abstract_input_type.AbstractInputTypeThe conductance input type
-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
e_rev_E¶
-
e_rev_I¶
-
get_global_weight_scale()[source]¶ Get the global weight scaling value.
Returns: The global weight scaling value Return type: float
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
get_values(parameters, state_variables, vertex_slice)[source]¶ Get the values to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of variables being retrieved
Returns: A list with the same length as self.struct.field_types
Return type: A list of (single value or list of values or RangedList)
- parameters (
-
has_variable(variable)[source]¶ Determine if this component has a variable by the given name
Parameters: variable (str) – The name of the variable Return type: bool
-
update_values(values, parameters, state_variables)[source]¶ Update the parameters and state variables with the given struct values that have been read from the machine
Parameters: - values – The values read from the machine, one for each struct element
- parameters – The holder of the parameters to update
- state_variables – The holder of the state variables to update
-
-
class
spynnaker.pyNN.models.neuron.input_types.input_type_current.InputTypeCurrent[source]¶ Bases:
spynnaker.pyNN.models.neuron.input_types.abstract_input_type.AbstractInputTypeThe current input type
-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
get_global_weight_scale()[source]¶ Get the global weight scaling value.
Returns: The global weight scaling value Return type: float
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
get_values(parameters, state_variables, vertex_slice)[source]¶ Get the values to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of variables being retrieved
Returns: A list with the same length as self.struct.field_types
Return type: A list of (single value or list of values or RangedList)
- parameters (
-
has_variable(variable)[source]¶ Determine if this component has a variable by the given name
Parameters: variable (str) – The name of the variable Return type: bool
-
update_values(values, parameters, state_variables)[source]¶ Update the parameters and state variables with the given struct values that have been read from the machine
Parameters: - values – The values read from the machine, one for each struct element
- parameters – The holder of the parameters to update
- state_variables – The holder of the state variables to update
-
-
class
spynnaker.pyNN.models.neuron.input_types.input_type_current_semd.InputTypeCurrentSEMD(multiplicator, inh_input_previous)[source]¶ Bases:
spynnaker.pyNN.models.neuron.input_types.abstract_input_type.AbstractInputTypeThe current sEMD input type
-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
get_global_weight_scale()[source]¶ Get the global weight scaling value.
Returns: The global weight scaling value Return type: float
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
get_values(parameters, state_variables, vertex_slice)[source]¶ Get the values to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of variables being retrieved
Returns: A list with the same length as self.struct.field_types
Return type: A list of (single value or list of values or RangedList)
- parameters (
-
has_variable(variable)[source]¶ Determine if this component has a variable by the given name
Parameters: variable (str) – The name of the variable Return type: bool
-
inh_input_previous¶
-
multiplicator¶
-
update_values(values, parameters, state_variables)[source]¶ Update the parameters and state variables with the given struct values that have been read from the machine
Parameters: - values – The values read from the machine, one for each struct element
- parameters – The holder of the parameters to update
- state_variables – The holder of the state variables to update
-
-
class
spynnaker.pyNN.models.neuron.input_types.AbstractInputType(data_types)[source]¶ -
Represents a possible input type for a neuron model (e.g., current).
Parameters: data_types – A list of data types in the component structure, in the order that they appear
-
class
spynnaker.pyNN.models.neuron.input_types.InputTypeConductance(e_rev_E, e_rev_I)[source]¶ Bases:
spynnaker.pyNN.models.neuron.input_types.abstract_input_type.AbstractInputTypeThe conductance input type
-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
e_rev_E¶
-
e_rev_I¶
-
get_global_weight_scale()[source]¶ Get the global weight scaling value.
Returns: The global weight scaling value Return type: float
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
get_values(parameters, state_variables, vertex_slice)[source]¶ Get the values to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of variables being retrieved
Returns: A list with the same length as self.struct.field_types
Return type: A list of (single value or list of values or RangedList)
- parameters (
-
has_variable(variable)[source]¶ Determine if this component has a variable by the given name
Parameters: variable (str) – The name of the variable Return type: bool
-
update_values(values, parameters, state_variables)[source]¶ Update the parameters and state variables with the given struct values that have been read from the machine
Parameters: - values – The values read from the machine, one for each struct element
- parameters – The holder of the parameters to update
- state_variables – The holder of the state variables to update
-
-
class
spynnaker.pyNN.models.neuron.input_types.InputTypeCurrent[source]¶ Bases:
spynnaker.pyNN.models.neuron.input_types.abstract_input_type.AbstractInputTypeThe current input type
-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
get_global_weight_scale()[source]¶ Get the global weight scaling value.
Returns: The global weight scaling value Return type: float
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
get_values(parameters, state_variables, vertex_slice)[source]¶ Get the values to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of variables being retrieved
Returns: A list with the same length as self.struct.field_types
Return type: A list of (single value or list of values or RangedList)
- parameters (
-
has_variable(variable)[source]¶ Determine if this component has a variable by the given name
Parameters: variable (str) – The name of the variable Return type: bool
-
update_values(values, parameters, state_variables)[source]¶ Update the parameters and state variables with the given struct values that have been read from the machine
Parameters: - values – The values read from the machine, one for each struct element
- parameters – The holder of the parameters to update
- state_variables – The holder of the state variables to update
-
-
class
spynnaker.pyNN.models.neuron.input_types.InputTypeCurrentSEMD(multiplicator, inh_input_previous)[source]¶ Bases:
spynnaker.pyNN.models.neuron.input_types.abstract_input_type.AbstractInputTypeThe current sEMD input type
-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
get_global_weight_scale()[source]¶ Get the global weight scaling value.
Returns: The global weight scaling value Return type: float
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
get_values(parameters, state_variables, vertex_slice)[source]¶ Get the values to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of variables being retrieved
Returns: A list with the same length as self.struct.field_types
Return type: A list of (single value or list of values or RangedList)
- parameters (
-
has_variable(variable)[source]¶ Determine if this component has a variable by the given name
Parameters: variable (str) – The name of the variable Return type: bool
-
inh_input_previous¶
-
multiplicator¶
-
update_values(values, parameters, state_variables)[source]¶ Update the parameters and state variables with the given struct values that have been read from the machine
Parameters: - values – The values read from the machine, one for each struct element
- parameters – The holder of the parameters to update
- state_variables – The holder of the state variables to update
-
-
class
spynnaker.pyNN.models.neuron.master_pop_table_generators.abstract_master_pop_table_factory.AbstractMasterPopTableFactory[source]¶ Bases:
object-
extract_synaptic_matrix_data_location(incoming_key, master_pop_base_mem_address, txrx, chip_x, chip_y)[source]¶ Parameters: - incoming_key (int) – the source key which the synaptic matrix needs to be mapped to
- master_pop_base_mem_address (int) – the base address of the master pop
- txrx (
spinnman.transceiver.Transceiver) – the transceiver object - chip_y (int) – the y coordinate of the chip of this master pop
- chip_x (int) – the x coordinate of the chip of this master pop
Returns: a synaptic matrix memory position.
-
finish_master_pop_table(spec, master_pop_table_region)[source]¶ Complete the master pop table in the data specification.
Parameters: - spec – the data specification to write the master pop entry to
- master_pop_table_region – the region to which the master pop table is being stored
-
get_edge_constraints()[source]¶ Gets the constraints for this table on edges coming in to a vertex.
Returns: a list of constraints Return type: list( pacman.model.constraints.AbstractConstraint)
-
get_master_population_table_size(vertex_slice, in_edges)[source]¶ Get the size of the master population table in SDRAM
-
update_master_population_table(spec, block_start_addr, row_length, key_and_mask, master_pop_table_region, is_single=False)[source]¶ Update a data specification with a master pop entry in some form
Parameters: - spec – the data specification to write the master pop entry to
- block_start_addr – the start address of the row in the region
- row_length – the row length of this entry
- key_and_mask (
pacman.model.routing_info.BaseKeyAndMask) – a key_and_mask object used as part of describing an edge that will require being received to be stored in the master pop table; the whole edge will become multiple calls to this function - master_pop_table_region – The region to which the master pop table is being stored
- is_single – True if this is a single synapse, False otherwise
-
-
class
spynnaker.pyNN.models.neuron.master_pop_table_generators.master_pop_table_as_binary_search.MasterPopTableAsBinarySearch[source]¶ -
Master population table, implemented as binary search master.
-
ADDRESS_LIST_DTYPE= '<u4'¶
-
ADDRESS_MASK= 2147483392¶
-
ADDRESS_SCALE= 16¶
-
ADDRESS_SCALED_SHIFT= 4¶
-
MASTER_POP_ENTRY_DTYPE= [('key', '<u4'), ('mask', '<u4'), ('start', '<u2'), ('count', '<u2')]¶
-
ROW_LENGTH_MASK= 255¶
-
SINGLE_BIT_FLAG_BIT= 2147483648¶
-
extract_synaptic_matrix_data_location(incoming_key, master_pop_base_mem_address, txrx, chip_x, chip_y)[source]¶ Parameters: - incoming_key (int) – the source key which the synaptic matrix needs to be mapped to
- master_pop_base_mem_address (int) – the base address of the master pop
- txrx (
spinnman.transceiver.Transceiver) – the transceiver object - chip_y (int) – the y coordinate of the chip of this master pop
- chip_x (int) – the x coordinate of the chip of this master pop
Returns: a synaptic matrix memory position.
-
finish_master_pop_table(spec, master_pop_table_region)[source]¶ Complete the master pop table in the data specification.
Parameters: - spec – the data specification to write the master pop entry to
- master_pop_table_region – the region to which the master pop table is being stored
-
get_allowed_row_length(row_length)[source]¶ Parameters: row_length – the row length being considered Returns: the row length available
-
get_edge_constraints()[source]¶ Gets the constraints for this table on edges coming in to a vertex.
Returns: a list of constraints Return type: list( pacman.model.constraints.AbstractConstraint)
-
get_exact_master_population_table_size(vertex, machine_graph, graph_mapper)[source]¶ Returns: the size the master pop table will take in SDRAM (in bytes)
-
get_master_population_table_size(vertex_slice, in_edges)[source]¶ Get the size of the master population table in SDRAM
Parameters: - vertex_slice – the slice of the vertex
- in_edges – the in coming edges
Returns: the size the master pop table will take in SDRAM (in bytes)
-
get_next_allowed_address(next_address)[source]¶ Parameters: next_address – The next address that would be used Returns: The next address that can be used following next_address
-
initialise_table(spec, master_population_table_region)[source]¶ Initialise the master pop data structure
Parameters: - spec – the DSG writer
- master_population_table_region – the region in memory that the master pop table will be written in
Return type: None
-
update_master_population_table(spec, block_start_addr, row_length, key_and_mask, master_pop_table_region, is_single=False)[source]¶ Add an entry in the binary search to deal with the synaptic matrix
Parameters: - spec – the writer for DSG
- block_start_addr – where the synaptic matrix block starts
- row_length – how long in bytes each synaptic entry is
- key_and_mask – the key and mask for this master pop entry
- master_pop_table_region – the region ID for the master pop
- is_single – Flag that states if the entry is a direct entry for a single row.
Returns: The index of the entry, to be used to retrieve it
Return type: int
-
-
class
spynnaker.pyNN.models.neuron.master_pop_table_generators.MasterPopTableAsBinarySearch[source]¶ -
Master population table, implemented as binary search master.
-
ADDRESS_LIST_DTYPE= '<u4'¶
-
ADDRESS_MASK= 2147483392¶
-
ADDRESS_SCALE= 16¶
-
ADDRESS_SCALED_SHIFT= 4¶
-
MASTER_POP_ENTRY_DTYPE= [('key', '<u4'), ('mask', '<u4'), ('start', '<u2'), ('count', '<u2')]¶
-
ROW_LENGTH_MASK= 255¶
-
SINGLE_BIT_FLAG_BIT= 2147483648¶
-
extract_synaptic_matrix_data_location(incoming_key, master_pop_base_mem_address, txrx, chip_x, chip_y)[source]¶ Parameters: - incoming_key (int) – the source key which the synaptic matrix needs to be mapped to
- master_pop_base_mem_address (int) – the base address of the master pop
- txrx (
spinnman.transceiver.Transceiver) – the transceiver object - chip_y (int) – the y coordinate of the chip of this master pop
- chip_x (int) – the x coordinate of the chip of this master pop
Returns: a synaptic matrix memory position.
-
finish_master_pop_table(spec, master_pop_table_region)[source]¶ Complete the master pop table in the data specification.
Parameters: - spec – the data specification to write the master pop entry to
- master_pop_table_region – the region to which the master pop table is being stored
-
get_allowed_row_length(row_length)[source]¶ Parameters: row_length – the row length being considered Returns: the row length available
-
get_edge_constraints()[source]¶ Gets the constraints for this table on edges coming in to a vertex.
Returns: a list of constraints Return type: list( pacman.model.constraints.AbstractConstraint)
-
get_exact_master_population_table_size(vertex, machine_graph, graph_mapper)[source]¶ Returns: the size the master pop table will take in SDRAM (in bytes)
-
get_master_population_table_size(vertex_slice, in_edges)[source]¶ Get the size of the master population table in SDRAM
Parameters: - vertex_slice – the slice of the vertex
- in_edges – the in coming edges
Returns: the size the master pop table will take in SDRAM (in bytes)
-
get_next_allowed_address(next_address)[source]¶ Parameters: next_address – The next address that would be used Returns: The next address that can be used following next_address
-
initialise_table(spec, master_population_table_region)[source]¶ Initialise the master pop data structure
Parameters: - spec – the DSG writer
- master_population_table_region – the region in memory that the master pop table will be written in
Return type: None
-
update_master_population_table(spec, block_start_addr, row_length, key_and_mask, master_pop_table_region, is_single=False)[source]¶ Add an entry in the binary search to deal with the synaptic matrix
Parameters: - spec – the writer for DSG
- block_start_addr – where the synaptic matrix block starts
- row_length – how long in bytes each synaptic entry is
- key_and_mask – the key and mask for this master pop entry
- master_pop_table_region – the region ID for the master pop
- is_single – Flag that states if the entry is a direct entry for a single row.
Returns: The index of the entry, to be used to retrieve it
Return type: int
-
-
class
spynnaker.pyNN.models.neuron.neuron_models.abstract_neuron_model.AbstractNeuronModel(data_types, global_data_types=None)[source]¶ -
Represents a neuron model.
Parameters: - data_types – A list of data types in the neuron structure, in the order that they appear
- global_data_types – A list of data types in the neuron global structure, in the order that they appear
-
get_data(parameters, state_variables, vertex_slice)[source]¶ Get the data to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of the vertex to generate parameters for
Return type: numpy array of uint32
- parameters (
-
get_dtcm_usage_in_bytes(n_neurons)[source]¶ Get the DTCM memory usage required
Parameters: n_neurons (int) – The number of neurons to get the usage for Return type: int
-
get_global_values()[source]¶ Get the global values to be written to the machine for this model
Returns: A list with the same length as self.global_struct.field_types Return type: A list of single values
-
get_sdram_usage_in_bytes(n_neurons)[source]¶ Get the SDRAM memory usage required
Parameters: n_neurons (int) – The number of neurons to get the usage for Return type: int
-
global_struct¶ Get the global parameters structure
-
read_data(data, offset, vertex_slice, parameters, state_variables)[source]¶ Read the parameters and state variables of the model from the given data
Parameters: - data – The data to be read
- offset – The offset where the data should be read from
- vertex_slice – The slice of the vertex to read parameters for
- parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters to update - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables to update
Returns: The offset after reading the data
-
class
spynnaker.pyNN.models.neuron.neuron_models.neuron_model_izh.NeuronModelIzh(a, b, c, d, v_init, u_init, i_offset)[source]¶ Bases:
spynnaker.pyNN.models.neuron.neuron_models.abstract_neuron_model.AbstractNeuronModel-
a¶
-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
b¶
-
c¶
-
d¶
-
get_global_values(machine_time_step)[source]¶ Get the global values to be written to the machine for this model
Returns: A list with the same length as self.global_struct.field_types Return type: A list of single values
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
get_values(parameters, state_variables, vertex_slice, ts)[source]¶ Get the values to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of variables being retrieved
Returns: A list with the same length as self.struct.field_types
Return type: A list of (single value or list of values or RangedList)
- parameters (
-
has_variable(variable)[source]¶ Determine if this component has a variable by the given name
Parameters: variable (str) – The name of the variable Return type: bool
-
i_offset¶
-
u_init¶
-
update_values(values, parameters, state_variables)[source]¶ Update the parameters and state variables with the given struct values that have been read from the machine
Parameters: - values – The values read from the machine, one for each struct element
- parameters – The holder of the parameters to update
- state_variables – The holder of the state variables to update
-
v_init¶
-
-
class
spynnaker.pyNN.models.neuron.neuron_models.neuron_model_leaky_integrate_and_fire.NeuronModelLeakyIntegrateAndFire(v_init, v_rest, tau_m, cm, i_offset, v_reset, tau_refrac)[source]¶ Bases:
spynnaker.pyNN.models.neuron.neuron_models.abstract_neuron_model.AbstractNeuronModel-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
cm¶
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
get_values(parameters, state_variables, vertex_slice, ts)[source]¶ Get the values to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of variables being retrieved
Returns: A list with the same length as self.struct.field_types
Return type: A list of (single value or list of values or RangedList)
- parameters (
-
has_variable(variable)[source]¶ Determine if this component has a variable by the given name
Parameters: variable (str) – The name of the variable Return type: bool
-
i_offset¶
-
tau_m¶
-
tau_refrac¶
-
update_values(values, parameters, state_variables)[source]¶ Update the parameters and state variables with the given struct values that have been read from the machine
Parameters: - values – The values read from the machine, one for each struct element
- parameters – The holder of the parameters to update
- state_variables – The holder of the state variables to update
-
v_init¶
-
v_reset¶
-
v_rest¶
-
-
class
spynnaker.pyNN.models.neuron.neuron_models.AbstractNeuronModel(data_types, global_data_types=None)[source]¶ -
Represents a neuron model.
Parameters: - data_types – A list of data types in the neuron structure, in the order that they appear
- global_data_types – A list of data types in the neuron global structure, in the order that they appear
-
get_data(parameters, state_variables, vertex_slice)[source]¶ Get the data to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of the vertex to generate parameters for
Return type: numpy array of uint32
- parameters (
-
get_dtcm_usage_in_bytes(n_neurons)[source]¶ Get the DTCM memory usage required
Parameters: n_neurons (int) – The number of neurons to get the usage for Return type: int
-
get_global_values()[source]¶ Get the global values to be written to the machine for this model
Returns: A list with the same length as self.global_struct.field_types Return type: A list of single values
-
get_sdram_usage_in_bytes(n_neurons)[source]¶ Get the SDRAM memory usage required
Parameters: n_neurons (int) – The number of neurons to get the usage for Return type: int
-
global_struct¶ Get the global parameters structure
-
read_data(data, offset, vertex_slice, parameters, state_variables)[source]¶ Read the parameters and state variables of the model from the given data
Parameters: - data – The data to be read
- offset – The offset where the data should be read from
- vertex_slice – The slice of the vertex to read parameters for
- parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters to update - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables to update
Returns: The offset after reading the data
-
class
spynnaker.pyNN.models.neuron.neuron_models.NeuronModelIzh(a, b, c, d, v_init, u_init, i_offset)[source]¶ Bases:
spynnaker.pyNN.models.neuron.neuron_models.abstract_neuron_model.AbstractNeuronModel-
a¶
-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
b¶
-
c¶
-
d¶
-
get_global_values(machine_time_step)[source]¶ Get the global values to be written to the machine for this model
Returns: A list with the same length as self.global_struct.field_types Return type: A list of single values
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
get_values(parameters, state_variables, vertex_slice, ts)[source]¶ Get the values to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of variables being retrieved
Returns: A list with the same length as self.struct.field_types
Return type: A list of (single value or list of values or RangedList)
- parameters (
-
has_variable(variable)[source]¶ Determine if this component has a variable by the given name
Parameters: variable (str) – The name of the variable Return type: bool
-
i_offset¶
-
u_init¶
-
update_values(values, parameters, state_variables)[source]¶ Update the parameters and state variables with the given struct values that have been read from the machine
Parameters: - values – The values read from the machine, one for each struct element
- parameters – The holder of the parameters to update
- state_variables – The holder of the state variables to update
-
v_init¶
-
-
class
spynnaker.pyNN.models.neuron.neuron_models.NeuronModelLeakyIntegrateAndFire(v_init, v_rest, tau_m, cm, i_offset, v_reset, tau_refrac)[source]¶ Bases:
spynnaker.pyNN.models.neuron.neuron_models.abstract_neuron_model.AbstractNeuronModel-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
cm¶
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
get_values(parameters, state_variables, vertex_slice, ts)[source]¶ Get the values to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of variables being retrieved
Returns: A list with the same length as self.struct.field_types
Return type: A list of (single value or list of values or RangedList)
- parameters (
-
has_variable(variable)[source]¶ Determine if this component has a variable by the given name
Parameters: variable (str) – The name of the variable Return type: bool
-
i_offset¶
-
tau_m¶
-
tau_refrac¶
-
update_values(values, parameters, state_variables)[source]¶ Update the parameters and state variables with the given struct values that have been read from the machine
Parameters: - values – The values read from the machine, one for each struct element
- parameters – The holder of the parameters to update
- state_variables – The holder of the state variables to update
-
v_init¶
-
v_reset¶
-
v_rest¶
-
-
spynnaker.pyNN.models.neuron.plasticity.stdp.common.plasticity_helpers.float_to_fixed(value, fixed_point_one)[source]¶
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.synapse_structure.AbstractSynapseStructure[source]¶ Bases:
object
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.synapse_structure.SynapseStructureWeightOnly[source]¶
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.abstract_timing_dependence.AbstractTimingDependence[source]¶ Bases:
object-
get_parameter_names()[source]¶ Return the names of the parameters supported by this timing dependency model.
Return type: iterable(str)
-
get_parameters_sdram_usage_in_bytes()[source]¶ Get the amount of SDRAM used by the parameters of this rule
-
n_weight_terms¶ The number of weight terms expected by this timing rule
-
pre_trace_n_bytes¶ The number of bytes used by the pre-trace of the rule per neuron
-
synaptic_structure¶ Get the synaptic structure of the plastic part of the rows
-
vertex_executable_suffix¶ The suffix to be appended to the vertex executable for this rule
-
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_pfister_spike_triplet.TimingDependencePfisterSpikeTriplet(tau_plus, tau_minus, tau_x, tau_y)[source]¶ -
-
get_parameter_names()[source]¶ Return the names of the parameters supported by this timing dependency model.
Return type: iterable(str)
-
get_parameters_sdram_usage_in_bytes()[source]¶ Get the amount of SDRAM used by the parameters of this rule
-
n_weight_terms¶ The number of weight terms expected by this timing rule
-
pre_trace_n_bytes¶ The number of bytes used by the pre-trace of the rule per neuron
-
synaptic_structure¶ Get the synaptic structure of the plastic part of the rows
-
tau_minus¶
-
tau_plus¶
-
tau_x¶
-
tau_y¶
-
vertex_executable_suffix¶ The suffix to be appended to the vertex executable for this rule
-
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_recurrent.TimingDependenceRecurrent(accumulator_depression=-6, accumulator_potentiation=6, mean_pre_window=35.0, mean_post_window=35.0, dual_fsm=True)[source]¶ -
-
default_parameters= {'accumulator_depression': -6, 'accumulator_potentiation': 6, 'dual_fsm': True, 'mean_post_window': 35.0, 'mean_pre_window': 35.0}¶
-
get_parameter_names()[source]¶ Return the names of the parameters supported by this timing dependency model.
Return type: iterable(str)
-
get_parameters_sdram_usage_in_bytes()[source]¶ Get the amount of SDRAM used by the parameters of this rule
-
n_weight_terms¶ The number of weight terms expected by this timing rule
-
pre_trace_n_bytes¶ The number of bytes used by the pre-trace of the rule per neuron
-
synaptic_structure¶ Get the synaptic structure of the plastic part of the rows
-
vertex_executable_suffix¶ The suffix to be appended to the vertex executable for this rule
-
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_spike_nearest_pair.TimingDependenceSpikeNearestPair(tau_plus=20.0, tau_minus=20.0)[source]¶ -
-
default_parameters= {'tau_minus': 20.0, 'tau_plus': 20.0}¶
-
get_parameter_names()[source]¶ Return the names of the parameters supported by this timing dependency model.
Return type: iterable(str)
-
get_parameters_sdram_usage_in_bytes()[source]¶ Get the amount of SDRAM used by the parameters of this rule
-
n_weight_terms¶ The number of weight terms expected by this timing rule
-
pre_trace_n_bytes¶ The number of bytes used by the pre-trace of the rule per neuron
-
synaptic_structure¶ Get the synaptic structure of the plastic part of the rows
-
tau_minus¶
-
tau_plus¶
-
vertex_executable_suffix¶ The suffix to be appended to the vertex executable for this rule
-
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_spike_pair.TimingDependenceSpikePair(tau_plus=20.0, tau_minus=20.0)[source]¶ -
-
get_parameter_names()[source]¶ Return the names of the parameters supported by this timing dependency model.
Return type: iterable(str)
-
get_parameters_sdram_usage_in_bytes()[source]¶ Get the amount of SDRAM used by the parameters of this rule
-
n_weight_terms¶ The number of weight terms expected by this timing rule
-
pre_trace_n_bytes¶ The number of bytes used by the pre-trace of the rule per neuron
-
synaptic_structure¶ Get the synaptic structure of the plastic part of the rows
-
tau_minus¶
-
tau_plus¶
-
vertex_executable_suffix¶ The suffix to be appended to the vertex executable for this rule
-
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_vogels_2011.TimingDependenceVogels2011(alpha, tau=20.0)[source]¶ -
-
alpha¶
-
default_parameters= {'tau': 20.0}¶
-
get_parameter_names()[source]¶ Return the names of the parameters supported by this timing dependency model.
Return type: iterable(str)
-
get_parameters_sdram_usage_in_bytes()[source]¶ Get the amount of SDRAM used by the parameters of this rule
-
n_weight_terms¶ The number of weight terms expected by this timing rule
-
pre_trace_n_bytes¶ The number of bytes used by the pre-trace of the rule per neuron
-
synaptic_structure¶ Get the synaptic structure of the plastic part of the rows
-
tau¶
-
vertex_executable_suffix¶ The suffix to be appended to the vertex executable for this rule
-
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.AbstractTimingDependence[source]¶ Bases:
object-
get_parameter_names()[source]¶ Return the names of the parameters supported by this timing dependency model.
Return type: iterable(str)
-
get_parameters_sdram_usage_in_bytes()[source]¶ Get the amount of SDRAM used by the parameters of this rule
-
n_weight_terms¶ The number of weight terms expected by this timing rule
-
pre_trace_n_bytes¶ The number of bytes used by the pre-trace of the rule per neuron
-
synaptic_structure¶ Get the synaptic structure of the plastic part of the rows
-
vertex_executable_suffix¶ The suffix to be appended to the vertex executable for this rule
-
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.TimingDependenceSpikePair(tau_plus=20.0, tau_minus=20.0)[source]¶ -
-
get_parameter_names()[source]¶ Return the names of the parameters supported by this timing dependency model.
Return type: iterable(str)
-
get_parameters_sdram_usage_in_bytes()[source]¶ Get the amount of SDRAM used by the parameters of this rule
-
n_weight_terms¶ The number of weight terms expected by this timing rule
-
pre_trace_n_bytes¶ The number of bytes used by the pre-trace of the rule per neuron
-
synaptic_structure¶ Get the synaptic structure of the plastic part of the rows
-
tau_minus¶
-
tau_plus¶
-
vertex_executable_suffix¶ The suffix to be appended to the vertex executable for this rule
-
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.TimingDependencePfisterSpikeTriplet(tau_plus, tau_minus, tau_x, tau_y)[source]¶ -
-
get_parameter_names()[source]¶ Return the names of the parameters supported by this timing dependency model.
Return type: iterable(str)
-
get_parameters_sdram_usage_in_bytes()[source]¶ Get the amount of SDRAM used by the parameters of this rule
-
n_weight_terms¶ The number of weight terms expected by this timing rule
-
pre_trace_n_bytes¶ The number of bytes used by the pre-trace of the rule per neuron
-
synaptic_structure¶ Get the synaptic structure of the plastic part of the rows
-
tau_minus¶
-
tau_plus¶
-
tau_x¶
-
tau_y¶
-
vertex_executable_suffix¶ The suffix to be appended to the vertex executable for this rule
-
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.TimingDependenceRecurrent(accumulator_depression=-6, accumulator_potentiation=6, mean_pre_window=35.0, mean_post_window=35.0, dual_fsm=True)[source]¶ -
-
default_parameters= {'accumulator_depression': -6, 'accumulator_potentiation': 6, 'dual_fsm': True, 'mean_post_window': 35.0, 'mean_pre_window': 35.0}¶
-
get_parameter_names()[source]¶ Return the names of the parameters supported by this timing dependency model.
Return type: iterable(str)
-
get_parameters_sdram_usage_in_bytes()[source]¶ Get the amount of SDRAM used by the parameters of this rule
-
n_weight_terms¶ The number of weight terms expected by this timing rule
-
pre_trace_n_bytes¶ The number of bytes used by the pre-trace of the rule per neuron
-
synaptic_structure¶ Get the synaptic structure of the plastic part of the rows
-
vertex_executable_suffix¶ The suffix to be appended to the vertex executable for this rule
-
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.TimingDependenceSpikeNearestPair(tau_plus=20.0, tau_minus=20.0)[source]¶ -
-
default_parameters= {'tau_minus': 20.0, 'tau_plus': 20.0}¶
-
get_parameter_names()[source]¶ Return the names of the parameters supported by this timing dependency model.
Return type: iterable(str)
-
get_parameters_sdram_usage_in_bytes()[source]¶ Get the amount of SDRAM used by the parameters of this rule
-
n_weight_terms¶ The number of weight terms expected by this timing rule
-
pre_trace_n_bytes¶ The number of bytes used by the pre-trace of the rule per neuron
-
synaptic_structure¶ Get the synaptic structure of the plastic part of the rows
-
tau_minus¶
-
tau_plus¶
-
vertex_executable_suffix¶ The suffix to be appended to the vertex executable for this rule
-
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.TimingDependenceVogels2011(alpha, tau=20.0)[source]¶ -
-
alpha¶
-
default_parameters= {'tau': 20.0}¶
-
get_parameter_names()[source]¶ Return the names of the parameters supported by this timing dependency model.
Return type: iterable(str)
-
get_parameters_sdram_usage_in_bytes()[source]¶ Get the amount of SDRAM used by the parameters of this rule
-
n_weight_terms¶ The number of weight terms expected by this timing rule
-
pre_trace_n_bytes¶ The number of bytes used by the pre-trace of the rule per neuron
-
synaptic_structure¶ Get the synaptic structure of the plastic part of the rows
-
tau¶
-
vertex_executable_suffix¶ The suffix to be appended to the vertex executable for this rule
-
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.abstract_weight_dependence.AbstractWeightDependence[source]¶ Bases:
object-
get_parameters_sdram_usage_in_bytes(n_synapse_types, n_weight_terms)[source]¶ Get the amount of SDRAM used by the parameters of this rule
-
get_provenance_data(pre_population_label, post_population_label)[source]¶ Get any provenance data
Parameters: - pre_population_label – label of pre.
- post_population_label – label of post.
Returns: the provenance data of the weight dependency
-
vertex_executable_suffix¶ The suffix to be appended to the vertex executable for this rule
-
weight_maximum¶ The maximum weight that will ever be set in a synapse as a result of this rule
-
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.weight_dependence_additive.WeightDependenceAdditive(w_min=0.0, w_max=1.0)[source]¶ Bases:
spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.abstract_has_a_plus_a_minus.AbstractHasAPlusAMinus,spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.abstract_weight_dependence.AbstractWeightDependence-
get_parameters_sdram_usage_in_bytes(n_synapse_types, n_weight_terms)[source]¶ Get the amount of SDRAM used by the parameters of this rule
-
vertex_executable_suffix¶ The suffix to be appended to the vertex executable for this rule
-
w_max¶
-
w_min¶
-
weight_maximum¶ The maximum weight that will ever be set in a synapse as a result of this rule
-
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.weight_dependence_additive_triplet.WeightDependenceAdditiveTriplet(w_min=0.0, w_max=1.0, A3_plus=0.01, A3_minus=0.01)[source]¶ Bases:
spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.abstract_has_a_plus_a_minus.AbstractHasAPlusAMinus,spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.abstract_weight_dependence.AbstractWeightDependence-
A3_minus¶
-
A3_plus¶
-
default_parameters= {'A3_minus': 0.01, 'A3_plus': 0.01, 'w_max': 1.0, 'w_min': 0.0}¶
-
get_parameters_sdram_usage_in_bytes(n_synapse_types, n_weight_terms)[source]¶ Get the amount of SDRAM used by the parameters of this rule
-
vertex_executable_suffix¶ The suffix to be appended to the vertex executable for this rule
-
w_max¶
-
w_min¶
-
weight_maximum¶ The maximum weight that will ever be set in a synapse as a result of this rule
-
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.weight_dependence_multiplicative.WeightDependenceMultiplicative(w_min=0.0, w_max=1.0)[source]¶ Bases:
spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.abstract_has_a_plus_a_minus.AbstractHasAPlusAMinus,spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.abstract_weight_dependence.AbstractWeightDependence-
get_parameters_sdram_usage_in_bytes(n_synapse_types, n_weight_terms)[source]¶ Get the amount of SDRAM used by the parameters of this rule
-
vertex_executable_suffix¶ The suffix to be appended to the vertex executable for this rule
-
w_max¶
-
w_min¶
-
weight_maximum¶ The maximum weight that will ever be set in a synapse as a result of this rule
-
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.AbstractHasAPlusAMinus[source]¶ Bases:
object-
A_minus¶
-
A_plus¶
-
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.AbstractWeightDependence[source]¶ Bases:
object-
get_parameters_sdram_usage_in_bytes(n_synapse_types, n_weight_terms)[source]¶ Get the amount of SDRAM used by the parameters of this rule
-
get_provenance_data(pre_population_label, post_population_label)[source]¶ Get any provenance data
Parameters: - pre_population_label – label of pre.
- post_population_label – label of post.
Returns: the provenance data of the weight dependency
-
vertex_executable_suffix¶ The suffix to be appended to the vertex executable for this rule
-
weight_maximum¶ The maximum weight that will ever be set in a synapse as a result of this rule
-
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.WeightDependenceAdditive(w_min=0.0, w_max=1.0)[source]¶ Bases:
spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.abstract_has_a_plus_a_minus.AbstractHasAPlusAMinus,spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.abstract_weight_dependence.AbstractWeightDependence-
get_parameters_sdram_usage_in_bytes(n_synapse_types, n_weight_terms)[source]¶ Get the amount of SDRAM used by the parameters of this rule
-
vertex_executable_suffix¶ The suffix to be appended to the vertex executable for this rule
-
w_max¶
-
w_min¶
-
weight_maximum¶ The maximum weight that will ever be set in a synapse as a result of this rule
-
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.WeightDependenceMultiplicative(w_min=0.0, w_max=1.0)[source]¶ Bases:
spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.abstract_has_a_plus_a_minus.AbstractHasAPlusAMinus,spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.abstract_weight_dependence.AbstractWeightDependence-
get_parameters_sdram_usage_in_bytes(n_synapse_types, n_weight_terms)[source]¶ Get the amount of SDRAM used by the parameters of this rule
-
vertex_executable_suffix¶ The suffix to be appended to the vertex executable for this rule
-
w_max¶
-
w_min¶
-
weight_maximum¶ The maximum weight that will ever be set in a synapse as a result of this rule
-
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.WeightDependenceAdditiveTriplet(w_min=0.0, w_max=1.0, A3_plus=0.01, A3_minus=0.01)[source]¶ Bases:
spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.abstract_has_a_plus_a_minus.AbstractHasAPlusAMinus,spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.abstract_weight_dependence.AbstractWeightDependence-
A3_minus¶
-
A3_plus¶
-
default_parameters= {'A3_minus': 0.01, 'A3_plus': 0.01, 'w_max': 1.0, 'w_min': 0.0}¶
-
get_parameters_sdram_usage_in_bytes(n_synapse_types, n_weight_terms)[source]¶ Get the amount of SDRAM used by the parameters of this rule
-
vertex_executable_suffix¶ The suffix to be appended to the vertex executable for this rule
-
w_max¶
-
w_min¶
-
weight_maximum¶ The maximum weight that will ever be set in a synapse as a result of this rule
-
-
class
spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_generate_on_machine.AbstractGenerateOnMachine[source]¶ Bases:
objectA synapse dynamics that can be generated on the machine
-
gen_matrix_id¶ The ID of the on-machine matrix generator
Return type: int
-
gen_matrix_params¶ Any parameters required by the matrix generator
Return type: numpy array of uint32
-
gen_matrix_params_size_in_bytes¶ The size of the parameters of the matrix generator in bytes
Return type: int
-
-
class
spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_plastic_synapse_dynamics.AbstractPlasticSynapseDynamics[source]¶ Bases:
spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_synapse_dynamics.AbstractSynapseDynamicsSynapses which change over time
-
get_n_fixed_plastic_words_per_row(fp_size)[source]¶ Get the number of fixed plastic words to be read from each row
-
get_n_plastic_plastic_words_per_row(pp_size)[source]¶ Get the number of plastic plastic words to be read from each row
-
get_n_synapses_in_rows(pp_size, fp_size)[source]¶ Get the number of synapses in each of the rows with plastic sizes pp_size and fp_size
-
get_n_words_for_plastic_connections(n_connections)[source]¶ Get the number of 32-bit words for n_connections in a single row
-
get_plastic_synaptic_data(connections, connection_row_indices, n_rows, post_vertex_slice, n_synapse_types)[source]¶ Get the fixed-plastic data, and plastic-plastic data for each row, and lengths for the fixed_plastic and plastic-plastic parts of each row.
Data is returned as an array made up of an array of 32-bit words for each row, for each of the fixed-plastic and plastic-plastic data regions. The row into which connection should go is given by connection_row_indices, and the total number of rows is given by n_rows.
Lengths are returned as an array made up of an integer for each row, for each of the fixed-plastic and plastic-plastic regions.
-
-
class
spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_static_synapse_dynamics.AbstractStaticSynapseDynamics[source]¶ Bases:
spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_synapse_dynamics.AbstractSynapseDynamicsDynamics which don’t change over time.
-
get_n_static_words_per_row(ff_size)[source]¶ Get the number of bytes to be read per row for the static data given the size that was written to each row
-
get_n_words_for_static_connections(n_connections)[source]¶ Get the number of 32-bit words for n_connections in a single row
-
get_static_synaptic_data(connections, connection_row_indices, n_rows, post_vertex_slice, n_synapse_types)[source]¶ Get the fixed-fixed data for each row, and lengths for the fixed-fixed parts of each row.
Data is returned as an array made up of an array of 32-bit words for each row for the fixed-fixed region. The row into which connection should go is given by connection_row_indices, and the total number of rows is given by n_rows.
Lengths are returned as an array made up of an integer for each row, for the fixed-fixed region.
-
-
class
spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_synapse_dynamics.AbstractSynapseDynamics[source]¶ Bases:
object-
NUMPY_CONNECTORS_DTYPE= [('source', 'uint32'), ('target', 'uint32'), ('weight', 'float64'), ('delay', 'float64')]¶
-
changes_during_run¶ Determine if the synapses change during a run
Return type: bool
-
convert_per_connection_data_to_rows(connection_row_indices, n_rows, data)[source]¶ Converts per-connection data generated from connections into row-based data to be returned from get_synaptic_data
-
get_max_synapses(n_words)[source]¶ Get the maximum number of synapses that can be held in the given number of words
Parameters: n_words – The number of words the synapses must fit in Return type: int
-
get_n_items(rows, item_size)[source]¶ Get the number of items in each row as 4-byte values, given the item size
-
get_parameter_names()[source]¶ Get the parameter names available from the synapse dynamics components
Return type: iterable(str)
-
get_parameters_sdram_usage_in_bytes(n_neurons, n_synapse_types)[source]¶ Get the SDRAM usage of the synapse dynamics parameters in bytes
-
-
class
spynnaker.pyNN.models.neuron.synapse_dynamics.structural_dynamics.StructuralDynamics(slow=None, fast=None, structure=None)[source]¶ Bases:
spynnaker.pyNN.models.neuron.synapse_dynamics.pynn_synapse_dynamics.PyNNSynapseDynamics-
structure¶
-
-
class
spynnaker.pyNN.models.neuron.synapse_dynamics.synapse_dynamics_static.SynapseDynamicsStatic(pad_to_length=None)[source]¶ Bases:
spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_static_synapse_dynamics.AbstractStaticSynapseDynamics,spynnaker.pyNN.models.abstract_models.abstract_settable.AbstractSettable,spinn_front_end_common.abstract_models.abstract_changable_after_run.AbstractChangableAfterRun,spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_generate_on_machine.AbstractGenerateOnMachine-
changes_during_run¶ Determine if the synapses change during a run
Return type: bool
-
gen_matrix_id¶ The ID of the on-machine matrix generator
Return type: int
-
get_max_synapses(n_words)[source]¶ Get the maximum number of synapses that can be held in the given number of words
Parameters: n_words – The number of words the synapses must fit in Return type: int
-
get_n_static_words_per_row(ff_size)[source]¶ Get the number of bytes to be read per row for the static data given the size that was written to each row
-
get_n_words_for_static_connections(n_connections)[source]¶ Get the number of 32-bit words for n_connections in a single row
-
get_parameter_names()[source]¶ Get the parameter names available from the synapse dynamics components
Return type: iterable(str)
-
get_parameters_sdram_usage_in_bytes(n_neurons, n_synapse_types)[source]¶ Get the SDRAM usage of the synapse dynamics parameters in bytes
-
get_static_synaptic_data(connections, connection_row_indices, n_rows, post_vertex_slice, n_synapse_types)[source]¶ Get the fixed-fixed data for each row, and lengths for the fixed-fixed parts of each row.
Data is returned as an array made up of an array of 32-bit words for each row for the fixed-fixed region. The row into which connection should go is given by connection_row_indices, and the total number of rows is given by n_rows.
Lengths are returned as an array made up of an integer for each row, for the fixed-fixed region.
-
mark_no_changes()[source]¶ Marks the point after which changes are reported, so that new changes can be detected before the next check. Marks the point after which changes are reported. Immediately after calling this method, requires_mapping should return False.
-
read_static_synaptic_data(post_vertex_slice, n_synapse_types, ff_size, ff_data)[source]¶ Read the connections from the words of data in ff_data
-
requires_mapping()[source]¶ True if changes that have been made require that mapping be performed. By default this returns False but can be overridden to indicate changes that require mapping.
Return type: bool True if changes that have been made require that mapping be performed. Note that this should return True the first time it is called, as the vertex must require mapping as it has been created!
-
-
class
spynnaker.pyNN.models.neuron.synapse_dynamics.synapse_dynamics_stdp.SynapseDynamicsSTDP(timing_dependence=None, weight_dependence=None, voltage_dependence=None, dendritic_delay_fraction=1.0, pad_to_length=None)[source]¶ Bases:
spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_plastic_synapse_dynamics.AbstractPlasticSynapseDynamics,spynnaker.pyNN.models.abstract_models.abstract_settable.AbstractSettable,spinn_front_end_common.abstract_models.abstract_changable_after_run.AbstractChangableAfterRun,spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_generate_on_machine.AbstractGenerateOnMachine-
changes_during_run¶ Determine if the synapses change during a run
Return type: bool
-
dendritic_delay_fraction¶
-
gen_matrix_id¶ The ID of the on-machine matrix generator
Return type: int
-
gen_matrix_params¶ Any parameters required by the matrix generator
Return type: numpy array of uint32
-
gen_matrix_params_size_in_bytes¶ The size of the parameters of the matrix generator in bytes
Return type: int
-
get_max_synapses(n_words)[source]¶ Get the maximum number of synapses that can be held in the given number of words
Parameters: n_words – The number of words the synapses must fit in Return type: int
-
get_n_fixed_plastic_words_per_row(fp_size)[source]¶ Get the number of fixed plastic words to be read from each row
-
get_n_plastic_plastic_words_per_row(pp_size)[source]¶ Get the number of plastic plastic words to be read from each row
-
get_n_synapses_in_rows(pp_size, fp_size)[source]¶ Get the number of synapses in each of the rows with plastic sizes pp_size and fp_size
-
get_n_words_for_plastic_connections(n_connections)[source]¶ Get the number of 32-bit words for n_connections in a single row
-
get_parameter_names()[source]¶ Get the parameter names available from the synapse dynamics components
Return type: iterable(str)
-
get_parameters_sdram_usage_in_bytes(n_neurons, n_synapse_types)[source]¶ Get the SDRAM usage of the synapse dynamics parameters in bytes
-
get_plastic_synaptic_data(connections, connection_row_indices, n_rows, post_vertex_slice, n_synapse_types)[source]¶ Get the fixed-plastic data, and plastic-plastic data for each row, and lengths for the fixed_plastic and plastic-plastic parts of each row.
Data is returned as an array made up of an array of 32-bit words for each row, for each of the fixed-plastic and plastic-plastic data regions. The row into which connection should go is given by connection_row_indices, and the total number of rows is given by n_rows.
Lengths are returned as an array made up of an integer for each row, for each of the fixed-plastic and plastic-plastic regions.
-
get_provenance_data(pre_population_label, post_population_label)[source]¶ Get the provenance data from this synapse dynamics object
-
mark_no_changes()[source]¶ Marks the point after which changes are reported, so that new changes can be detected before the next check. Marks the point after which changes are reported. Immediately after calling this method, requires_mapping should return False.
-
read_plastic_synaptic_data(post_vertex_slice, n_synapse_types, pp_size, pp_data, fp_size, fp_data)[source]¶ Read the connections indicated in the connection indices from the data in pp_data and fp_data
-
requires_mapping()[source]¶ True if changes that have been made require that mapping be performed. By default this returns False but can be overridden to indicate changes that require mapping.
Return type: bool True if changes that have been made require that mapping be performed. Note that this should return True the first time it is called, as the vertex must require mapping as it has been created!
-
set_value(key, value)[source]¶ Set a property
Parameters: - key – the name of the parameter to change
- value – the new value of the parameter to assign Set a property
- key – the name of the parameter to change
- value – the new value of the parameter to assign
-
timing_dependence¶
-
weight_dependence¶
-
-
class
spynnaker.pyNN.models.neuron.synapse_dynamics.synapse_dynamics_structural_common.SynapseDynamicsStructuralCommon(stdp_model=None, f_rew=10000, weight=0, delay=1, s_max=32, sigma_form_forward=2.5, sigma_form_lateral=1, p_form_forward=0.16, p_form_lateral=1.0, p_elim_dep=0.0245, p_elim_pot=0.00013600000000000003, grid=array([16, 16]), lateral_inhibition=0, random_partner=False, seed=None)[source]¶ -
Common class that enables synaptic rewiring. It acts as a wrapper around SynapseDynamicsStatic or SynapseDynamicsSTDP. This means rewiring can operate in parallel with these types of synapses.
Written by Petrut Bogdan.Parameters: - f_rew (int) – Frequency of rewiring (Hz). How many rewiring attempts will be done per second.
- weight (float) – Initial weight assigned to a newly formed connection
- delay (int) – Delay assigned to a newly formed connection
- s_max (int) – Maximum fan-in per target layer neuron
- sigma_form_forward (float) – Spread of feed-forward formation receptive field
- sigma_form_lateral (float) – Spread of lateral formation receptive field
- p_form_forward (float) – Peak probability for feed-forward formation
- p_form_lateral (float) – Peak probability for lateral formation
- p_elim_pot (float) – Probability of elimination of a potentiated synapse
- p_elim_dep (float) – Probability of elimination of a depressed synapse
- grid (2d int array) – Grid shape
- lateral_inhibition (bool) – Flag whether to mark synapses formed within a layer as inhibitory or excitatory
- random_partner (bool) – Flag whether to randomly select pre-synaptic partner for formation
- seed (int) – seed the random number generators
-
actual_sdram_usage¶ Actual SDRAM usage (based on what is written to spec).
Returns: actual SDRAM usage Return type: int
-
default_parameters= {'delay': 1, 'f_rew': 10000, 'grid': array([16, 16]), 'lateral_inhibition': 0, 'p_elim_dep': 0.0245, 'p_elim_pot': 0.00013600000000000003, 'p_form_forward': 0.16, 'p_form_lateral': 1.0, 'random_partner': False, 's_max': 32, 'sigma_form_forward': 2.5, 'sigma_form_lateral': 1, 'stdp_model': None, 'weight': 0}¶
-
distance(x0, x1, grid=array([16, 16]), type='euclidian')[source]¶ Compute the distance between points x0 and x1 place on the grid using periodic boundary conditions.
Parameters: - x0 (np.ndarray of ints) – first point in space
- x1 (np.ndarray of ints) – second point in space
- grid (np.ndarray of ints) – shape of grid
- type (str) – distance metric, i.e. euclidian or manhattan
Returns: the distance
Return type: float
-
generate_distance_probability_array(probability, sigma)[source]¶ Generate the exponentially decaying probability LUTs.
Parameters: - probability (float) – peak probability
- sigma (float) – spread
Returns: distance-dependent probabilities
Return type: numpy.ndarray(float)
-
get_extra_sdram_usage_in_bytes(machine_in_edges)[source]¶ Better approximation of SDRAM usage based on incoming machine edges
Parameters: machine_in_edges (machine edges) – incoming machine edges Returns: SDRAM usage Return type: int
-
get_parameters_sdram_usage_in_bytes(n_neurons, n_synapse_types, in_edges)[source]¶ Approximate SDRAM usage
Parameters: - n_neurons (int) – number of neurons
- n_synapse_types (int) – number of synapse types (i.e. excitatory and inhibitory)
- in_edges (edges) – incoming edges
Returns: SDRAM usage
Return type: int
-
p_rew¶ The period of rewiring.
Returns: The period of rewiring Return type: int
-
synaptic_data_update(connections, post_vertex_slice, app_edge, machine_edge)[source]¶ Get static synaptic data
-
weight_dynamics¶
-
write_parameters(spec, region, machine_time_step, weight_scales, application_graph, machine_graph, app_vertex, post_slice, machine_vertex, graph_mapper, routing_info)[source]¶ Write the synapse parameters to the spec.
Parameters: - spec (spec) – the data spec
- region (int) – memory region
- machine_time_step (int) – the duration of a machine time step (ms)
- weight_scales (list(float)) – scaling the weights
- application_graph (
ApplicationGraph) – the entire, highest level, graph of the network to be simulated - machine_graph (
MachineGraph) – the entire, lowest level, graph of the network to be simulated - app_vertex (
ApplicationVertex) – the highest level object of the post-synaptic population - post_slice (
Slice) – the slice of the app vertex corresponding to this machine vertex - machine_vertex (
MachineVertex) – the lowest level object of the post-synaptic population - graph_mapper (
GraphMapper) – for looking up application vertices - routing_info (
RoutingInfo) – All of the routing information on the network
Returns: None
Return type: None
-
class
spynnaker.pyNN.models.neuron.synapse_dynamics.synapse_dynamics_structural_static.SynapseDynamicsStructuralStatic(stdp_model=None, f_rew=10000, weight=0, delay=1, s_max=32, sigma_form_forward=2.5, sigma_form_lateral=1, p_form_forward=0.16, p_form_lateral=1.0, p_elim_dep=0.0245, p_elim_pot=0.00013600000000000003, grid=array([16, 16]), lateral_inhibition=0, random_partner=False, seed=None)[source]¶ Bases:
spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_synapse_dynamics_structural.AbstractSynapseDynamicsStructural,spynnaker.pyNN.models.neuron.synapse_dynamics.synapse_dynamics_static.SynapseDynamicsStatic- Class that enables synaptic rewiring. It acts as a wrapper around SynapseDynamicsStatic.
This means rewiring can operate in parallel with these types of synapses.
Written by Petrut Bogdan.
Example usage to allow rewiring in parallel with STDP:
stdp_model = sim.STDPMechanism(...) structure_model_with_stdp = sim.StructuralMechanismStatic( weight=0, s_max=32, grid=[np.sqrt(pop_size), np.sqrt(pop_size)], random_partner=True, f_rew=10 ** 4, # Hz sigma_form_forward=1., delay=10 ) plastic_projection = sim.Projection( ..., synapse_dynamics=sim.SynapseDynamics( slow=structure_model_with_stdp ) )
Parameters: - f_rew (int) – Frequency of rewiring (Hz). How many rewiring attempts will be done per second.
- weight (float) – Initial weight assigned to a newly formed connection
- delay (int) – Delay assigned to a newly formed connection
- s_max (int) – Maximum fan-in per target layer neuron
- sigma_form_forward (float) – Spread of feed-forward formation receptive field
- sigma_form_lateral (float) – Spread of lateral formation receptive field
- p_form_forward (float) – Peak probability for feed-forward formation
- p_form_lateral (float) – Peak probability for lateral formation
- p_elim_pot (float) – Probability of elimination of a potentiated synapse
- p_elim_dep (float) – Probability of elimination of a depressed synapse
- grid (2d int array) – Grid shape
- lateral_inhibition (bool) – Flag whether to mark synapses formed within a layer as inhibitory or excitatory
- random_partner (bool) – Flag whether to randomly select pre-synaptic partner for formation
- seed (int) – seed the random number generators
-
changes_during_run¶ Determine if the synapses change during a run
Return type: bool
-
get_n_words_for_static_connections(n_connections)[source]¶ Get the number of 32-bit words for n_connections in a single row
-
get_parameter_names()[source]¶ Get the parameter names available from the synapse dynamics components
Return type: iterable(str)
-
get_parameters_sdram_usage_in_bytes(n_neurons, n_synapse_types, in_edges)[source]¶ Get the SDRAM usage of the synapse dynamics parameters in bytes
-
get_static_synaptic_data(connections, connection_row_indices, n_rows, post_vertex_slice, n_synapse_types, app_edge, machine_edge)[source]¶ Get the fixed-fixed data for each row, and lengths for the fixed-fixed parts of each row.
Data is returned as an array made up of an array of 32-bit words for each row for the fixed-fixed region. The row into which connection should go is given by connection_row_indices, and the total number of rows is given by n_rows.
Lengths are returned as an array made up of an integer for each row, for the fixed-fixed region.
-
class
spynnaker.pyNN.models.neuron.synapse_dynamics.synapse_dynamics_structural_stdp.SynapseDynamicsStructuralSTDP(stdp_model=None, f_rew=10000, weight=0, delay=1, s_max=32, sigma_form_forward=2.5, sigma_form_lateral=1, p_form_forward=0.16, p_form_lateral=1.0, p_elim_dep=0.0245, p_elim_pot=0.00013600000000000003, grid=array([16, 16]), lateral_inhibition=0, random_partner=False, seed=None)[source]¶ Bases:
spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_synapse_dynamics_structural.AbstractSynapseDynamicsStructural,spynnaker.pyNN.models.neuron.synapse_dynamics.synapse_dynamics_stdp.SynapseDynamicsSTDP- Class that enables synaptic rewiring. It acts as a wrapper
around SynapseDynamicsSTDP. This means rewiring can operate in parallel with these types of synapses.
Written by Petrut Bogdan.
Example usage to allow rewiring in parallel with STDP:
stdp_model = sim.STDPMechanism(...) structure_model_with_stdp = sim.StructuralMechanismSTDP( stdp_model=stdp_model, weight=0, s_max=32, grid=[np.sqrt(pop_size), np.sqrt(pop_size)], random_partner=True, f_rew=10 ** 4, # Hz sigma_form_forward=1., delay=10 ) plastic_projection = sim.Projection( ..., synapse_dynamics=sim.SynapseDynamics( slow=structure_model_with_stdp ) )
Parameters: - f_rew (int) – Frequency of rewiring (Hz). How many rewiring attempts will be done per second.
- weight (float) – Initial weight assigned to a newly formed connection
- delay (int) – Delay assigned to a newly formed connection
- s_max (int) – Maximum fan-in per target layer neuron
- sigma_form_forward (float) – Spread of feed-forward formation receptive field
- sigma_form_lateral (float) – Spread of lateral formation receptive field
- p_form_forward (float) – Peak probability for feed-forward formation
- p_form_lateral (float) – Peak probability for lateral formation
- p_elim_pot (float) – Probability of elimination of a potentiated synapse
- p_elim_dep (float) – Probability of elimination of a depressed synapse
- grid (2d int array) – Grid shape
- lateral_inhibition (bool) – Flag whether to mark synapses formed within a layer as inhibitory or excitatory
- random_partner (bool) – Flag whether to randomly select pre-synaptic partner for formation
- seed (int) – seed the random number generators
-
get_n_words_for_plastic_connections(n_connections)[source]¶ Get the number of 32-bit words for n_connections in a single row
-
get_parameter_names()[source]¶ Get the parameter names available from the synapse dynamics components
Return type: iterable(str)
-
get_parameters_sdram_usage_in_bytes(n_neurons, n_synapse_types, in_edges)[source]¶ Get the SDRAM usage of the synapse dynamics parameters in bytes
-
get_plastic_synaptic_data(connections, connection_row_indices, n_rows, post_vertex_slice, n_synapse_types, app_edge, machine_edge)[source]¶ Get the fixed-plastic data, and plastic-plastic data for each row, and lengths for the fixed_plastic and plastic-plastic parts of each row.
Data is returned as an array made up of an array of 32-bit words for each row, for each of the fixed-plastic and plastic-plastic data regions. The row into which connection should go is given by connection_row_indices, and the total number of rows is given by n_rows.
Lengths are returned as an array made up of an integer for each row, for each of the fixed-plastic and plastic-plastic regions.
-
class
spynnaker.pyNN.models.neuron.synapse_dynamics.AbstractSynapseDynamics[source]¶ Bases:
object-
NUMPY_CONNECTORS_DTYPE= [('source', 'uint32'), ('target', 'uint32'), ('weight', 'float64'), ('delay', 'float64')]¶
-
changes_during_run¶ Determine if the synapses change during a run
Return type: bool
-
convert_per_connection_data_to_rows(connection_row_indices, n_rows, data)[source]¶ Converts per-connection data generated from connections into row-based data to be returned from get_synaptic_data
-
get_max_synapses(n_words)[source]¶ Get the maximum number of synapses that can be held in the given number of words
Parameters: n_words – The number of words the synapses must fit in Return type: int
-
get_n_items(rows, item_size)[source]¶ Get the number of items in each row as 4-byte values, given the item size
-
get_parameter_names()[source]¶ Get the parameter names available from the synapse dynamics components
Return type: iterable(str)
-
get_parameters_sdram_usage_in_bytes(n_neurons, n_synapse_types)[source]¶ Get the SDRAM usage of the synapse dynamics parameters in bytes
-
-
class
spynnaker.pyNN.models.neuron.synapse_dynamics.AbstractGenerateOnMachine[source]¶ Bases:
objectA synapse dynamics that can be generated on the machine
-
gen_matrix_id¶ The ID of the on-machine matrix generator
Return type: int
-
gen_matrix_params¶ Any parameters required by the matrix generator
Return type: numpy array of uint32
-
gen_matrix_params_size_in_bytes¶ The size of the parameters of the matrix generator in bytes
Return type: int
-
-
class
spynnaker.pyNN.models.neuron.synapse_dynamics.AbstractStaticSynapseDynamics[source]¶ Bases:
spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_synapse_dynamics.AbstractSynapseDynamicsDynamics which don’t change over time.
-
get_n_static_words_per_row(ff_size)[source]¶ Get the number of bytes to be read per row for the static data given the size that was written to each row
-
get_n_words_for_static_connections(n_connections)[source]¶ Get the number of 32-bit words for n_connections in a single row
-
get_static_synaptic_data(connections, connection_row_indices, n_rows, post_vertex_slice, n_synapse_types)[source]¶ Get the fixed-fixed data for each row, and lengths for the fixed-fixed parts of each row.
Data is returned as an array made up of an array of 32-bit words for each row for the fixed-fixed region. The row into which connection should go is given by connection_row_indices, and the total number of rows is given by n_rows.
Lengths are returned as an array made up of an integer for each row, for the fixed-fixed region.
-
-
class
spynnaker.pyNN.models.neuron.synapse_dynamics.AbstractPlasticSynapseDynamics[source]¶ Bases:
spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_synapse_dynamics.AbstractSynapseDynamicsSynapses which change over time
-
get_n_fixed_plastic_words_per_row(fp_size)[source]¶ Get the number of fixed plastic words to be read from each row
-
get_n_plastic_plastic_words_per_row(pp_size)[source]¶ Get the number of plastic plastic words to be read from each row
-
get_n_synapses_in_rows(pp_size, fp_size)[source]¶ Get the number of synapses in each of the rows with plastic sizes pp_size and fp_size
-
get_n_words_for_plastic_connections(n_connections)[source]¶ Get the number of 32-bit words for n_connections in a single row
-
get_plastic_synaptic_data(connections, connection_row_indices, n_rows, post_vertex_slice, n_synapse_types)[source]¶ Get the fixed-plastic data, and plastic-plastic data for each row, and lengths for the fixed_plastic and plastic-plastic parts of each row.
Data is returned as an array made up of an array of 32-bit words for each row, for each of the fixed-plastic and plastic-plastic data regions. The row into which connection should go is given by connection_row_indices, and the total number of rows is given by n_rows.
Lengths are returned as an array made up of an integer for each row, for each of the fixed-plastic and plastic-plastic regions.
-
-
class
spynnaker.pyNN.models.neuron.synapse_dynamics.PyNNSynapseDynamics(slow=None, fast=None)[source]¶ Bases:
object-
slow¶
-
-
class
spynnaker.pyNN.models.neuron.synapse_dynamics.SynapseDynamicsStatic(pad_to_length=None)[source]¶ Bases:
spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_static_synapse_dynamics.AbstractStaticSynapseDynamics,spynnaker.pyNN.models.abstract_models.abstract_settable.AbstractSettable,spinn_front_end_common.abstract_models.abstract_changable_after_run.AbstractChangableAfterRun,spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_generate_on_machine.AbstractGenerateOnMachine-
changes_during_run¶ Determine if the synapses change during a run
Return type: bool
-
gen_matrix_id¶ The ID of the on-machine matrix generator
Return type: int
-
get_max_synapses(n_words)[source]¶ Get the maximum number of synapses that can be held in the given number of words
Parameters: n_words – The number of words the synapses must fit in Return type: int
-
get_n_static_words_per_row(ff_size)[source]¶ Get the number of bytes to be read per row for the static data given the size that was written to each row
-
get_n_words_for_static_connections(n_connections)[source]¶ Get the number of 32-bit words for n_connections in a single row
-
get_parameter_names()[source]¶ Get the parameter names available from the synapse dynamics components
Return type: iterable(str)
-
get_parameters_sdram_usage_in_bytes(n_neurons, n_synapse_types)[source]¶ Get the SDRAM usage of the synapse dynamics parameters in bytes
-
get_static_synaptic_data(connections, connection_row_indices, n_rows, post_vertex_slice, n_synapse_types)[source]¶ Get the fixed-fixed data for each row, and lengths for the fixed-fixed parts of each row.
Data is returned as an array made up of an array of 32-bit words for each row for the fixed-fixed region. The row into which connection should go is given by connection_row_indices, and the total number of rows is given by n_rows.
Lengths are returned as an array made up of an integer for each row, for the fixed-fixed region.
-
mark_no_changes()[source]¶ Marks the point after which changes are reported, so that new changes can be detected before the next check. Marks the point after which changes are reported. Immediately after calling this method, requires_mapping should return False.
-
read_static_synaptic_data(post_vertex_slice, n_synapse_types, ff_size, ff_data)[source]¶ Read the connections from the words of data in ff_data
-
requires_mapping()[source]¶ True if changes that have been made require that mapping be performed. By default this returns False but can be overridden to indicate changes that require mapping.
Return type: bool True if changes that have been made require that mapping be performed. Note that this should return True the first time it is called, as the vertex must require mapping as it has been created!
-
-
class
spynnaker.pyNN.models.neuron.synapse_dynamics.SynapseDynamicsSTDP(timing_dependence=None, weight_dependence=None, voltage_dependence=None, dendritic_delay_fraction=1.0, pad_to_length=None)[source]¶ Bases:
spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_plastic_synapse_dynamics.AbstractPlasticSynapseDynamics,spynnaker.pyNN.models.abstract_models.abstract_settable.AbstractSettable,spinn_front_end_common.abstract_models.abstract_changable_after_run.AbstractChangableAfterRun,spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_generate_on_machine.AbstractGenerateOnMachine-
changes_during_run¶ Determine if the synapses change during a run
Return type: bool
-
dendritic_delay_fraction¶
-
gen_matrix_id¶ The ID of the on-machine matrix generator
Return type: int
-
gen_matrix_params¶ Any parameters required by the matrix generator
Return type: numpy array of uint32
-
gen_matrix_params_size_in_bytes¶ The size of the parameters of the matrix generator in bytes
Return type: int
-
get_max_synapses(n_words)[source]¶ Get the maximum number of synapses that can be held in the given number of words
Parameters: n_words – The number of words the synapses must fit in Return type: int
-
get_n_fixed_plastic_words_per_row(fp_size)[source]¶ Get the number of fixed plastic words to be read from each row
-
get_n_plastic_plastic_words_per_row(pp_size)[source]¶ Get the number of plastic plastic words to be read from each row
-
get_n_synapses_in_rows(pp_size, fp_size)[source]¶ Get the number of synapses in each of the rows with plastic sizes pp_size and fp_size
-
get_n_words_for_plastic_connections(n_connections)[source]¶ Get the number of 32-bit words for n_connections in a single row
-
get_parameter_names()[source]¶ Get the parameter names available from the synapse dynamics components
Return type: iterable(str)
-
get_parameters_sdram_usage_in_bytes(n_neurons, n_synapse_types)[source]¶ Get the SDRAM usage of the synapse dynamics parameters in bytes
-
get_plastic_synaptic_data(connections, connection_row_indices, n_rows, post_vertex_slice, n_synapse_types)[source]¶ Get the fixed-plastic data, and plastic-plastic data for each row, and lengths for the fixed_plastic and plastic-plastic parts of each row.
Data is returned as an array made up of an array of 32-bit words for each row, for each of the fixed-plastic and plastic-plastic data regions. The row into which connection should go is given by connection_row_indices, and the total number of rows is given by n_rows.
Lengths are returned as an array made up of an integer for each row, for each of the fixed-plastic and plastic-plastic regions.
-
get_provenance_data(pre_population_label, post_population_label)[source]¶ Get the provenance data from this synapse dynamics object
-
mark_no_changes()[source]¶ Marks the point after which changes are reported, so that new changes can be detected before the next check. Marks the point after which changes are reported. Immediately after calling this method, requires_mapping should return False.
-
read_plastic_synaptic_data(post_vertex_slice, n_synapse_types, pp_size, pp_data, fp_size, fp_data)[source]¶ Read the connections indicated in the connection indices from the data in pp_data and fp_data
-
requires_mapping()[source]¶ True if changes that have been made require that mapping be performed. By default this returns False but can be overridden to indicate changes that require mapping.
Return type: bool True if changes that have been made require that mapping be performed. Note that this should return True the first time it is called, as the vertex must require mapping as it has been created!
-
set_value(key, value)[source]¶ Set a property
Parameters: - key – the name of the parameter to change
- value – the new value of the parameter to assign Set a property
- key – the name of the parameter to change
- value – the new value of the parameter to assign
-
timing_dependence¶
-
weight_dependence¶
-
-
class
spynnaker.pyNN.models.neuron.synapse_dynamics.AbstractSynapseDynamicsStructural[source]¶ Bases:
object
-
class
spynnaker.pyNN.models.neuron.synapse_dynamics.StructuralDynamics(slow=None, fast=None, structure=None)[source]¶ Bases:
spynnaker.pyNN.models.neuron.synapse_dynamics.pynn_synapse_dynamics.PyNNSynapseDynamics-
structure¶
-
-
class
spynnaker.pyNN.models.neuron.synapse_dynamics.SynapseDynamicsStructuralCommon(stdp_model=None, f_rew=10000, weight=0, delay=1, s_max=32, sigma_form_forward=2.5, sigma_form_lateral=1, p_form_forward=0.16, p_form_lateral=1.0, p_elim_dep=0.0245, p_elim_pot=0.00013600000000000003, grid=array([16, 16]), lateral_inhibition=0, random_partner=False, seed=None)[source]¶ -
Common class that enables synaptic rewiring. It acts as a wrapper around SynapseDynamicsStatic or SynapseDynamicsSTDP. This means rewiring can operate in parallel with these types of synapses.
Written by Petrut Bogdan.Parameters: - f_rew (int) – Frequency of rewiring (Hz). How many rewiring attempts will be done per second.
- weight (float) – Initial weight assigned to a newly formed connection
- delay (int) – Delay assigned to a newly formed connection
- s_max (int) – Maximum fan-in per target layer neuron
- sigma_form_forward (float) – Spread of feed-forward formation receptive field
- sigma_form_lateral (float) – Spread of lateral formation receptive field
- p_form_forward (float) – Peak probability for feed-forward formation
- p_form_lateral (float) – Peak probability for lateral formation
- p_elim_pot (float) – Probability of elimination of a potentiated synapse
- p_elim_dep (float) – Probability of elimination of a depressed synapse
- grid (2d int array) – Grid shape
- lateral_inhibition (bool) – Flag whether to mark synapses formed within a layer as inhibitory or excitatory
- random_partner (bool) – Flag whether to randomly select pre-synaptic partner for formation
- seed (int) – seed the random number generators
-
actual_sdram_usage¶ Actual SDRAM usage (based on what is written to spec).
Returns: actual SDRAM usage Return type: int
-
default_parameters= {'delay': 1, 'f_rew': 10000, 'grid': array([16, 16]), 'lateral_inhibition': 0, 'p_elim_dep': 0.0245, 'p_elim_pot': 0.00013600000000000003, 'p_form_forward': 0.16, 'p_form_lateral': 1.0, 'random_partner': False, 's_max': 32, 'sigma_form_forward': 2.5, 'sigma_form_lateral': 1, 'stdp_model': None, 'weight': 0}¶
-
distance(x0, x1, grid=array([16, 16]), type='euclidian')[source]¶ Compute the distance between points x0 and x1 place on the grid using periodic boundary conditions.
Parameters: - x0 (np.ndarray of ints) – first point in space
- x1 (np.ndarray of ints) – second point in space
- grid (np.ndarray of ints) – shape of grid
- type (str) – distance metric, i.e. euclidian or manhattan
Returns: the distance
Return type: float
-
generate_distance_probability_array(probability, sigma)[source]¶ Generate the exponentially decaying probability LUTs.
Parameters: - probability (float) – peak probability
- sigma (float) – spread
Returns: distance-dependent probabilities
Return type: numpy.ndarray(float)
-
get_extra_sdram_usage_in_bytes(machine_in_edges)[source]¶ Better approximation of SDRAM usage based on incoming machine edges
Parameters: machine_in_edges (machine edges) – incoming machine edges Returns: SDRAM usage Return type: int
-
get_parameters_sdram_usage_in_bytes(n_neurons, n_synapse_types, in_edges)[source]¶ Approximate SDRAM usage
Parameters: - n_neurons (int) – number of neurons
- n_synapse_types (int) – number of synapse types (i.e. excitatory and inhibitory)
- in_edges (edges) – incoming edges
Returns: SDRAM usage
Return type: int
-
p_rew¶ The period of rewiring.
Returns: The period of rewiring Return type: int
-
synaptic_data_update(connections, post_vertex_slice, app_edge, machine_edge)[source]¶ Get static synaptic data
-
weight_dynamics¶
-
write_parameters(spec, region, machine_time_step, weight_scales, application_graph, machine_graph, app_vertex, post_slice, machine_vertex, graph_mapper, routing_info)[source]¶ Write the synapse parameters to the spec.
Parameters: - spec (spec) – the data spec
- region (int) – memory region
- machine_time_step (int) – the duration of a machine time step (ms)
- weight_scales (list(float)) – scaling the weights
- application_graph (
ApplicationGraph) – the entire, highest level, graph of the network to be simulated - machine_graph (
MachineGraph) – the entire, lowest level, graph of the network to be simulated - app_vertex (
ApplicationVertex) – the highest level object of the post-synaptic population - post_slice (
Slice) – the slice of the app vertex corresponding to this machine vertex - machine_vertex (
MachineVertex) – the lowest level object of the post-synaptic population - graph_mapper (
GraphMapper) – for looking up application vertices - routing_info (
RoutingInfo) – All of the routing information on the network
Returns: None
Return type: None
-
class
spynnaker.pyNN.models.neuron.synapse_dynamics.SynapseDynamicsStructuralStatic(stdp_model=None, f_rew=10000, weight=0, delay=1, s_max=32, sigma_form_forward=2.5, sigma_form_lateral=1, p_form_forward=0.16, p_form_lateral=1.0, p_elim_dep=0.0245, p_elim_pot=0.00013600000000000003, grid=array([16, 16]), lateral_inhibition=0, random_partner=False, seed=None)[source]¶ Bases:
spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_synapse_dynamics_structural.AbstractSynapseDynamicsStructural,spynnaker.pyNN.models.neuron.synapse_dynamics.synapse_dynamics_static.SynapseDynamicsStatic- Class that enables synaptic rewiring. It acts as a wrapper around SynapseDynamicsStatic.
This means rewiring can operate in parallel with these types of synapses.
Written by Petrut Bogdan.
Example usage to allow rewiring in parallel with STDP:
stdp_model = sim.STDPMechanism(...) structure_model_with_stdp = sim.StructuralMechanismStatic( weight=0, s_max=32, grid=[np.sqrt(pop_size), np.sqrt(pop_size)], random_partner=True, f_rew=10 ** 4, # Hz sigma_form_forward=1., delay=10 ) plastic_projection = sim.Projection( ..., synapse_dynamics=sim.SynapseDynamics( slow=structure_model_with_stdp ) )
Parameters: - f_rew (int) – Frequency of rewiring (Hz). How many rewiring attempts will be done per second.
- weight (float) – Initial weight assigned to a newly formed connection
- delay (int) – Delay assigned to a newly formed connection
- s_max (int) – Maximum fan-in per target layer neuron
- sigma_form_forward (float) – Spread of feed-forward formation receptive field
- sigma_form_lateral (float) – Spread of lateral formation receptive field
- p_form_forward (float) – Peak probability for feed-forward formation
- p_form_lateral (float) – Peak probability for lateral formation
- p_elim_pot (float) – Probability of elimination of a potentiated synapse
- p_elim_dep (float) – Probability of elimination of a depressed synapse
- grid (2d int array) – Grid shape
- lateral_inhibition (bool) – Flag whether to mark synapses formed within a layer as inhibitory or excitatory
- random_partner (bool) – Flag whether to randomly select pre-synaptic partner for formation
- seed (int) – seed the random number generators
-
changes_during_run¶ Determine if the synapses change during a run
Return type: bool
-
get_n_words_for_static_connections(n_connections)[source]¶ Get the number of 32-bit words for n_connections in a single row
-
get_parameter_names()[source]¶ Get the parameter names available from the synapse dynamics components
Return type: iterable(str)
-
get_parameters_sdram_usage_in_bytes(n_neurons, n_synapse_types, in_edges)[source]¶ Get the SDRAM usage of the synapse dynamics parameters in bytes
-
get_static_synaptic_data(connections, connection_row_indices, n_rows, post_vertex_slice, n_synapse_types, app_edge, machine_edge)[source]¶ Get the fixed-fixed data for each row, and lengths for the fixed-fixed parts of each row.
Data is returned as an array made up of an array of 32-bit words for each row for the fixed-fixed region. The row into which connection should go is given by connection_row_indices, and the total number of rows is given by n_rows.
Lengths are returned as an array made up of an integer for each row, for the fixed-fixed region.
-
class
spynnaker.pyNN.models.neuron.synapse_dynamics.SynapseDynamicsStructuralSTDP(stdp_model=None, f_rew=10000, weight=0, delay=1, s_max=32, sigma_form_forward=2.5, sigma_form_lateral=1, p_form_forward=0.16, p_form_lateral=1.0, p_elim_dep=0.0245, p_elim_pot=0.00013600000000000003, grid=array([16, 16]), lateral_inhibition=0, random_partner=False, seed=None)[source]¶ Bases:
spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_synapse_dynamics_structural.AbstractSynapseDynamicsStructural,spynnaker.pyNN.models.neuron.synapse_dynamics.synapse_dynamics_stdp.SynapseDynamicsSTDP- Class that enables synaptic rewiring. It acts as a wrapper
around SynapseDynamicsSTDP. This means rewiring can operate in parallel with these types of synapses.
Written by Petrut Bogdan.
Example usage to allow rewiring in parallel with STDP:
stdp_model = sim.STDPMechanism(...) structure_model_with_stdp = sim.StructuralMechanismSTDP( stdp_model=stdp_model, weight=0, s_max=32, grid=[np.sqrt(pop_size), np.sqrt(pop_size)], random_partner=True, f_rew=10 ** 4, # Hz sigma_form_forward=1., delay=10 ) plastic_projection = sim.Projection( ..., synapse_dynamics=sim.SynapseDynamics( slow=structure_model_with_stdp ) )
Parameters: - f_rew (int) – Frequency of rewiring (Hz). How many rewiring attempts will be done per second.
- weight (float) – Initial weight assigned to a newly formed connection
- delay (int) – Delay assigned to a newly formed connection
- s_max (int) – Maximum fan-in per target layer neuron
- sigma_form_forward (float) – Spread of feed-forward formation receptive field
- sigma_form_lateral (float) – Spread of lateral formation receptive field
- p_form_forward (float) – Peak probability for feed-forward formation
- p_form_lateral (float) – Peak probability for lateral formation
- p_elim_pot (float) – Probability of elimination of a potentiated synapse
- p_elim_dep (float) – Probability of elimination of a depressed synapse
- grid (2d int array) – Grid shape
- lateral_inhibition (bool) – Flag whether to mark synapses formed within a layer as inhibitory or excitatory
- random_partner (bool) – Flag whether to randomly select pre-synaptic partner for formation
- seed (int) – seed the random number generators
-
get_n_words_for_plastic_connections(n_connections)[source]¶ Get the number of 32-bit words for n_connections in a single row
-
get_parameter_names()[source]¶ Get the parameter names available from the synapse dynamics components
Return type: iterable(str)
-
get_parameters_sdram_usage_in_bytes(n_neurons, n_synapse_types, in_edges)[source]¶ Get the SDRAM usage of the synapse dynamics parameters in bytes
-
get_plastic_synaptic_data(connections, connection_row_indices, n_rows, post_vertex_slice, n_synapse_types, app_edge, machine_edge)[source]¶ Get the fixed-plastic data, and plastic-plastic data for each row, and lengths for the fixed_plastic and plastic-plastic parts of each row.
Data is returned as an array made up of an array of 32-bit words for each row, for each of the fixed-plastic and plastic-plastic data regions. The row into which connection should go is given by connection_row_indices, and the total number of rows is given by n_rows.
Lengths are returned as an array made up of an integer for each row, for each of the fixed-plastic and plastic-plastic regions.
-
class
spynnaker.pyNN.models.neuron.synapse_io.abstract_synapse_io.AbstractSynapseIO[source]¶ Bases:
object-
get_block_n_bytes(max_row_length, n_rows)[source]¶ Get the number of bytes in a block given the max row length and number of rows
-
get_max_row_info(synapse_info, post_vertex_slice, n_delay_stages, population_table, machine_time_step, in_edge)[source]¶ Get the information about the maximum lengths of delayed and undelayed rows in bytes (including header), words (without header) and number of synapses
-
get_maximum_delay_supported_in_ms(machine_time_step)[source]¶ Get the maximum delay supported by the synapse representation before extensions are required, or None if any delay is supported
-
get_synapses(synapse_info, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, n_delay_stages, population_table, n_synapse_types, weight_scales, machine_time_step, app_edge, machine_edge)[source]¶ Get the synapses as an array of words for non-delayed synapses and an array of words for delayed synapses
-
-
class
spynnaker.pyNN.models.neuron.synapse_io.max_row_info.MaxRowInfo(undelayed_max_n_synapses, delayed_max_n_synapses, undelayed_max_bytes, delayed_max_bytes, undelayed_max_words, delayed_max_words)[source]¶ Bases:
objectInformation about the maximums for rows in a synaptic matrix.
-
delayed_max_bytes¶
-
delayed_max_n_synapses¶
-
delayed_max_words¶
-
undelayed_max_bytes¶
-
undelayed_max_n_synapses¶
-
undelayed_max_words¶
-
-
class
spynnaker.pyNN.models.neuron.synapse_io.synapse_io_row_based.SynapseIORowBased[source]¶ Bases:
spynnaker.pyNN.models.neuron.synapse_io.abstract_synapse_io.AbstractSynapseIOA SynapseRowIO implementation that uses a row for each source neuron, where each row consists of a fixed region, a plastic region, and a fixed-plastic region (this is the bits of the plastic row that don’t actually change). The plastic region structure is determined by the synapse dynamics of the connector.
-
get_block_n_bytes(max_row_length, n_rows)[source]¶ Get the number of bytes in a block given the max row length and number of rows
-
get_max_row_info(synapse_info, post_vertex_slice, n_delay_stages, population_table, machine_time_step, in_edge)[source]¶ Get the information about the maximum lengths of delayed and undelayed rows in bytes (including header), words (without header) and number of synapses
-
get_maximum_delay_supported_in_ms(machine_time_step)[source]¶ Get the maximum delay supported by the synapse representation before extensions are required, or None if any delay is supported
-
get_synapses(synapse_info, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, n_delay_stages, population_table, n_synapse_types, weight_scales, machine_time_step, app_edge, machine_edge)[source]¶ Get the synapses as an array of words for non-delayed synapses and an array of words for delayed synapses
-
-
class
spynnaker.pyNN.models.neuron.synapse_io.AbstractSynapseIO[source]¶ Bases:
object-
get_block_n_bytes(max_row_length, n_rows)[source]¶ Get the number of bytes in a block given the max row length and number of rows
-
get_max_row_info(synapse_info, post_vertex_slice, n_delay_stages, population_table, machine_time_step, in_edge)[source]¶ Get the information about the maximum lengths of delayed and undelayed rows in bytes (including header), words (without header) and number of synapses
-
get_maximum_delay_supported_in_ms(machine_time_step)[source]¶ Get the maximum delay supported by the synapse representation before extensions are required, or None if any delay is supported
-
get_synapses(synapse_info, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, n_delay_stages, population_table, n_synapse_types, weight_scales, machine_time_step, app_edge, machine_edge)[source]¶ Get the synapses as an array of words for non-delayed synapses and an array of words for delayed synapses
-
-
class
spynnaker.pyNN.models.neuron.synapse_io.SynapseIORowBased[source]¶ Bases:
spynnaker.pyNN.models.neuron.synapse_io.abstract_synapse_io.AbstractSynapseIOA SynapseRowIO implementation that uses a row for each source neuron, where each row consists of a fixed region, a plastic region, and a fixed-plastic region (this is the bits of the plastic row that don’t actually change). The plastic region structure is determined by the synapse dynamics of the connector.
-
get_block_n_bytes(max_row_length, n_rows)[source]¶ Get the number of bytes in a block given the max row length and number of rows
-
get_max_row_info(synapse_info, post_vertex_slice, n_delay_stages, population_table, machine_time_step, in_edge)[source]¶ Get the information about the maximum lengths of delayed and undelayed rows in bytes (including header), words (without header) and number of synapses
-
get_maximum_delay_supported_in_ms(machine_time_step)[source]¶ Get the maximum delay supported by the synapse representation before extensions are required, or None if any delay is supported
-
get_synapses(synapse_info, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, n_delay_stages, population_table, n_synapse_types, weight_scales, machine_time_step, app_edge, machine_edge)[source]¶ Get the synapses as an array of words for non-delayed synapses and an array of words for delayed synapses
-
-
class
spynnaker.pyNN.models.neuron.synapse_types.abstract_synapse_type.AbstractSynapseType(data_types)[source]¶ -
Represents the synapse types supported.
Parameters: data_types – A list of data types in the component structure, in the order that they appear -
get_n_synapse_types()[source]¶ Get the number of synapse types supported.
Returns: The number of synapse types supported Return type: int
-
-
class
spynnaker.pyNN.models.neuron.synapse_types.synapse_type_alpha.SynapseTypeAlpha(exc_response, exc_exp_response, tau_syn_E, inh_response, inh_exp_response, tau_syn_I)[source]¶ Bases:
spynnaker.pyNN.models.neuron.synapse_types.abstract_synapse_type.AbstractSynapseType-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
exc_response¶
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_n_synapse_types()[source]¶ Get the number of synapse types supported.
Returns: The number of synapse types supported Return type: int
-
get_synapse_id_by_target(target)[source]¶ Get the ID of a synapse given the name.
Returns: The ID of the synapse Return type: int
-
get_synapse_targets()[source]¶ Get the target names of the synapse type.
Returns: an array of strings Return type: array(str)
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
get_values(parameters, state_variables, vertex_slice, ts)[source]¶ Get the values to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of variables being retrieved
Returns: A list with the same length as self.struct.field_types
Return type: A list of (single value or list of values or RangedList)
- parameters (
-
has_variable(variable)[source]¶ Determine if this component has a variable by the given name
Parameters: variable (str) – The name of the variable Return type: bool
-
inh_response¶
-
tau_syn_E¶
-
tau_syn_I¶
-
update_values(values, parameters, state_variables)[source]¶ Update the parameters and state variables with the given struct values that have been read from the machine
Parameters: - values – The values read from the machine, one for each struct element
- parameters – The holder of the parameters to update
- state_variables – The holder of the state variables to update
-
-
class
spynnaker.pyNN.models.neuron.synapse_types.synapse_type_delta.SynapseTypeDelta(isyn_exc, isyn_inh)[source]¶ Bases:
spynnaker.pyNN.models.neuron.synapse_types.abstract_synapse_type.AbstractSynapseTypeThis represents a synapse type with two delta synapses
-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_n_synapse_types()[source]¶ Get the number of synapse types supported.
Returns: The number of synapse types supported Return type: int
-
get_synapse_id_by_target(target)[source]¶ Get the ID of a synapse given the name.
Returns: The ID of the synapse Return type: int
-
get_synapse_targets()[source]¶ Get the target names of the synapse type.
Returns: an array of strings Return type: array(str)
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
get_values(parameters, state_variables, vertex_slice)[source]¶ Get the values to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of variables being retrieved
Returns: A list with the same length as self.struct.field_types
Return type: A list of (single value or list of values or RangedList)
- parameters (
-
has_variable(variable)[source]¶ Determine if this component has a variable by the given name
Parameters: variable (str) – The name of the variable Return type: bool
-
isyn_exc¶
-
isyn_inh¶
-
update_values(values, parameters, state_variables)[source]¶ Update the parameters and state variables with the given struct values that have been read from the machine
Parameters: - values – The values read from the machine, one for each struct element
- parameters – The holder of the parameters to update
- state_variables – The holder of the state variables to update
-
-
class
spynnaker.pyNN.models.neuron.synapse_types.synapse_type_dual_exponential.SynapseTypeDualExponential(tau_syn_E, tau_syn_E2, tau_syn_I, isyn_exc, isyn_exc2, isyn_inh)[source]¶ Bases:
spynnaker.pyNN.models.neuron.synapse_types.abstract_synapse_type.AbstractSynapseType-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_n_synapse_types()[source]¶ Get the number of synapse types supported.
Returns: The number of synapse types supported Return type: int
-
get_synapse_id_by_target(target)[source]¶ Get the ID of a synapse given the name.
Returns: The ID of the synapse Return type: int
-
get_synapse_targets()[source]¶ Get the target names of the synapse type.
Returns: an array of strings Return type: array(str)
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
get_values(parameters, state_variables, vertex_slice, ts)[source]¶ Get the values to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of variables being retrieved
Returns: A list with the same length as self.struct.field_types
Return type: A list of (single value or list of values or RangedList)
- parameters (
-
has_variable(variable)[source]¶ Determine if this component has a variable by the given name
Parameters: variable (str) – The name of the variable Return type: bool
-
isyn_exc¶
-
isyn_exc2¶
-
isyn_inh¶
-
tau_syn_E¶
-
tau_syn_E2¶
-
tau_syn_I¶
-
update_values(values, parameters, state_variables)[source]¶ Update the parameters and state variables with the given struct values that have been read from the machine
Parameters: - values – The values read from the machine, one for each struct element
- parameters – The holder of the parameters to update
- state_variables – The holder of the state variables to update
-
-
class
spynnaker.pyNN.models.neuron.synapse_types.synapse_type_exponential.SynapseTypeExponential(tau_syn_E, tau_syn_I, isyn_exc, isyn_inh)[source]¶ Bases:
spynnaker.pyNN.models.neuron.synapse_types.abstract_synapse_type.AbstractSynapseType-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_n_synapse_types()[source]¶ Get the number of synapse types supported.
Returns: The number of synapse types supported Return type: int
-
get_synapse_id_by_target(target)[source]¶ Get the ID of a synapse given the name.
Returns: The ID of the synapse Return type: int
-
get_synapse_targets()[source]¶ Get the target names of the synapse type.
Returns: an array of strings Return type: array(str)
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
get_values(parameters, state_variables, vertex_slice, ts)[source]¶ Get the values to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of variables being retrieved
Returns: A list with the same length as self.struct.field_types
Return type: A list of (single value or list of values or RangedList)
- parameters (
-
has_variable(variable)[source]¶ Determine if this component has a variable by the given name
Parameters: variable (str) – The name of the variable Return type: bool
-
isyn_exc¶
-
isyn_inh¶
-
tau_syn_E¶
-
tau_syn_I¶
-
update_values(values, parameters, state_variables)[source]¶ Update the parameters and state variables with the given struct values that have been read from the machine
Parameters: - values – The values read from the machine, one for each struct element
- parameters – The holder of the parameters to update
- state_variables – The holder of the state variables to update
-
-
class
spynnaker.pyNN.models.neuron.synapse_types.AbstractSynapseType(data_types)[source]¶ -
Represents the synapse types supported.
Parameters: data_types – A list of data types in the component structure, in the order that they appear -
get_n_synapse_types()[source]¶ Get the number of synapse types supported.
Returns: The number of synapse types supported Return type: int
-
-
class
spynnaker.pyNN.models.neuron.synapse_types.SynapseTypeDualExponential(tau_syn_E, tau_syn_E2, tau_syn_I, isyn_exc, isyn_exc2, isyn_inh)[source]¶ Bases:
spynnaker.pyNN.models.neuron.synapse_types.abstract_synapse_type.AbstractSynapseType-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_n_synapse_types()[source]¶ Get the number of synapse types supported.
Returns: The number of synapse types supported Return type: int
-
get_synapse_id_by_target(target)[source]¶ Get the ID of a synapse given the name.
Returns: The ID of the synapse Return type: int
-
get_synapse_targets()[source]¶ Get the target names of the synapse type.
Returns: an array of strings Return type: array(str)
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
get_values(parameters, state_variables, vertex_slice, ts)[source]¶ Get the values to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of variables being retrieved
Returns: A list with the same length as self.struct.field_types
Return type: A list of (single value or list of values or RangedList)
- parameters (
-
has_variable(variable)[source]¶ Determine if this component has a variable by the given name
Parameters: variable (str) – The name of the variable Return type: bool
-
isyn_exc¶
-
isyn_exc2¶
-
isyn_inh¶
-
tau_syn_E¶
-
tau_syn_E2¶
-
tau_syn_I¶
-
update_values(values, parameters, state_variables)[source]¶ Update the parameters and state variables with the given struct values that have been read from the machine
Parameters: - values – The values read from the machine, one for each struct element
- parameters – The holder of the parameters to update
- state_variables – The holder of the state variables to update
-
-
class
spynnaker.pyNN.models.neuron.synapse_types.SynapseTypeExponential(tau_syn_E, tau_syn_I, isyn_exc, isyn_inh)[source]¶ Bases:
spynnaker.pyNN.models.neuron.synapse_types.abstract_synapse_type.AbstractSynapseType-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_n_synapse_types()[source]¶ Get the number of synapse types supported.
Returns: The number of synapse types supported Return type: int
-
get_synapse_id_by_target(target)[source]¶ Get the ID of a synapse given the name.
Returns: The ID of the synapse Return type: int
-
get_synapse_targets()[source]¶ Get the target names of the synapse type.
Returns: an array of strings Return type: array(str)
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
get_values(parameters, state_variables, vertex_slice, ts)[source]¶ Get the values to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of variables being retrieved
Returns: A list with the same length as self.struct.field_types
Return type: A list of (single value or list of values or RangedList)
- parameters (
-
has_variable(variable)[source]¶ Determine if this component has a variable by the given name
Parameters: variable (str) – The name of the variable Return type: bool
-
isyn_exc¶
-
isyn_inh¶
-
tau_syn_E¶
-
tau_syn_I¶
-
update_values(values, parameters, state_variables)[source]¶ Update the parameters and state variables with the given struct values that have been read from the machine
Parameters: - values – The values read from the machine, one for each struct element
- parameters – The holder of the parameters to update
- state_variables – The holder of the state variables to update
-
-
class
spynnaker.pyNN.models.neuron.synapse_types.SynapseTypeDelta(isyn_exc, isyn_inh)[source]¶ Bases:
spynnaker.pyNN.models.neuron.synapse_types.abstract_synapse_type.AbstractSynapseTypeThis represents a synapse type with two delta synapses
-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_n_synapse_types()[source]¶ Get the number of synapse types supported.
Returns: The number of synapse types supported Return type: int
-
get_synapse_id_by_target(target)[source]¶ Get the ID of a synapse given the name.
Returns: The ID of the synapse Return type: int
-
get_synapse_targets()[source]¶ Get the target names of the synapse type.
Returns: an array of strings Return type: array(str)
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
get_values(parameters, state_variables, vertex_slice)[source]¶ Get the values to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of variables being retrieved
Returns: A list with the same length as self.struct.field_types
Return type: A list of (single value or list of values or RangedList)
- parameters (
-
has_variable(variable)[source]¶ Determine if this component has a variable by the given name
Parameters: variable (str) – The name of the variable Return type: bool
-
isyn_exc¶
-
isyn_inh¶
-
update_values(values, parameters, state_variables)[source]¶ Update the parameters and state variables with the given struct values that have been read from the machine
Parameters: - values – The values read from the machine, one for each struct element
- parameters – The holder of the parameters to update
- state_variables – The holder of the state variables to update
-
-
class
spynnaker.pyNN.models.neuron.synapse_types.SynapseTypeAlpha(exc_response, exc_exp_response, tau_syn_E, inh_response, inh_exp_response, tau_syn_I)[source]¶ Bases:
spynnaker.pyNN.models.neuron.synapse_types.abstract_synapse_type.AbstractSynapseType-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
exc_response¶
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_n_synapse_types()[source]¶ Get the number of synapse types supported.
Returns: The number of synapse types supported Return type: int
-
get_synapse_id_by_target(target)[source]¶ Get the ID of a synapse given the name.
Returns: The ID of the synapse Return type: int
-
get_synapse_targets()[source]¶ Get the target names of the synapse type.
Returns: an array of strings Return type: array(str)
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
get_values(parameters, state_variables, vertex_slice, ts)[source]¶ Get the values to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of variables being retrieved
Returns: A list with the same length as self.struct.field_types
Return type: A list of (single value or list of values or RangedList)
- parameters (
-
has_variable(variable)[source]¶ Determine if this component has a variable by the given name
Parameters: variable (str) – The name of the variable Return type: bool
-
inh_response¶
-
tau_syn_E¶
-
tau_syn_I¶
-
update_values(values, parameters, state_variables)[source]¶ Update the parameters and state variables with the given struct values that have been read from the machine
Parameters: - values – The values read from the machine, one for each struct element
- parameters – The holder of the parameters to update
- state_variables – The holder of the state variables to update
-
-
class
spynnaker.pyNN.models.neuron.threshold_types.threshold_type_maass_stochastic.ThresholdTypeMaassStochastic(du_th, tau_th, v_thresh)[source]¶ Bases:
spynnaker.pyNN.models.neuron.threshold_types.abstract_threshold_type.AbstractThresholdTypeA stochastic threshold
-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
du_th¶
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
get_values(parameters, state_variables, vertex_slice, ts)[source]¶ Get the values to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of variables being retrieved
Returns: A list with the same length as self.struct.field_types
Return type: A list of (single value or list of values or RangedList)
- parameters (
-
has_variable(variable)[source]¶ Determine if this component has a variable by the given name
Parameters: variable (str) – The name of the variable Return type: bool
-
tau_th¶
-
update_values(values, parameters, state_variables)[source]¶ Update the parameters and state variables with the given struct values that have been read from the machine
Parameters: - values – The values read from the machine, one for each struct element
- parameters – The holder of the parameters to update
- state_variables – The holder of the state variables to update
-
v_thresh¶
-
-
class
spynnaker.pyNN.models.neuron.threshold_types.threshold_type_static.ThresholdTypeStatic(v_thresh)[source]¶ Bases:
spynnaker.pyNN.models.neuron.threshold_types.abstract_threshold_type.AbstractThresholdTypeA threshold that is a static value
-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
get_values(parameters, state_variables, vertex_slice)[source]¶ Get the values to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of variables being retrieved
Returns: A list with the same length as self.struct.field_types
Return type: A list of (single value or list of values or RangedList)
- parameters (
-
has_variable(variable)[source]¶ Determine if this component has a variable by the given name
Parameters: variable (str) – The name of the variable Return type: bool
-
update_values(values, parameters, state_variables)[source]¶ Update the parameters and state variables with the given struct values that have been read from the machine
Parameters: - values – The values read from the machine, one for each struct element
- parameters – The holder of the parameters to update
- state_variables – The holder of the state variables to update
-
v_thresh¶
-
-
class
spynnaker.pyNN.models.neuron.threshold_types.AbstractThresholdType(data_types)[source]¶ -
Represents types of threshold for a neuron (e.g., stochastic).
Parameters: data_types – A list of data types in the component structure, in the order that they appear
-
class
spynnaker.pyNN.models.neuron.threshold_types.ThresholdTypeStatic(v_thresh)[source]¶ Bases:
spynnaker.pyNN.models.neuron.threshold_types.abstract_threshold_type.AbstractThresholdTypeA threshold that is a static value
-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
get_values(parameters, state_variables, vertex_slice)[source]¶ Get the values to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of variables being retrieved
Returns: A list with the same length as self.struct.field_types
Return type: A list of (single value or list of values or RangedList)
- parameters (
-
has_variable(variable)[source]¶ Determine if this component has a variable by the given name
Parameters: variable (str) – The name of the variable Return type: bool
-
update_values(values, parameters, state_variables)[source]¶ Update the parameters and state variables with the given struct values that have been read from the machine
Parameters: - values – The values read from the machine, one for each struct element
- parameters – The holder of the parameters to update
- state_variables – The holder of the state variables to update
-
v_thresh¶
-
-
class
spynnaker.pyNN.models.neuron.threshold_types.ThresholdTypeMaassStochastic(du_th, tau_th, v_thresh)[source]¶ Bases:
spynnaker.pyNN.models.neuron.threshold_types.abstract_threshold_type.AbstractThresholdTypeA stochastic threshold
-
add_parameters(parameters)[source]¶ Add the initial values of the parameters to the parameter holder
Parameters: parameters ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the parameters
-
add_state_variables(state_variables)[source]¶ Add the initial values of the state variables to the state variables holder
Parameters: state_variables ( spinn_utilities.ranged.range_dictionary.RangeDictionary) – A holder of the state variables
-
du_th¶
-
get_n_cpu_cycles(n_neurons)[source]¶ Get the number of CPU cycles required to update the state
Parameters: n_neurons (int) – The number of neurons to get the cycles for Return type: int
-
get_units(variable)[source]¶ Get the units of the given variable
Parameters: variable (str) – The name of the variable
-
get_values(parameters, state_variables, vertex_slice, ts)[source]¶ Get the values to be written to the machine for this model
Parameters: - parameters (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the parameters - state_variables (
spinn_utilities.ranged.range_dictionary.RangeDictionary) – The holder of the state variables - vertex_slice – The slice of variables being retrieved
Returns: A list with the same length as self.struct.field_types
Return type: A list of (single value or list of values or RangedList)
- parameters (
-
has_variable(variable)[source]¶ Determine if this component has a variable by the given name
Parameters: variable (str) – The name of the variable Return type: bool
-
tau_th¶
-
update_values(values, parameters, state_variables)[source]¶ Update the parameters and state variables with the given struct values that have been read from the machine
Parameters: - values – The values read from the machine, one for each struct element
- parameters – The holder of the parameters to update
- state_variables – The holder of the state variables to update
-
v_thresh¶
-
-
class
spynnaker.pyNN.models.neuron.abstract_population_vertex.AbstractPopulationVertex(n_neurons, label, constraints, max_atoms_per_core, spikes_per_second, ring_buffer_sigma, incoming_spike_buffer_size, neuron_impl, pynn_model)[source]¶ Bases:
pacman.model.graphs.application.application_vertex.ApplicationVertex,spinn_front_end_common.abstract_models.abstract_generates_data_specification.AbstractGeneratesDataSpecification,spinn_front_end_common.abstract_models.abstract_has_associated_binary.AbstractHasAssociatedBinary,spynnaker.pyNN.models.abstract_models.abstract_contains_units.AbstractContainsUnits,spynnaker.pyNN.models.common.abstract_spike_recordable.AbstractSpikeRecordable,spynnaker.pyNN.models.common.abstract_neuron_recordable.AbstractNeuronRecordable,spinn_front_end_common.abstract_models.abstract_provides_outgoing_partition_constraints.AbstractProvidesOutgoingPartitionConstraints,spinn_front_end_common.abstract_models.abstract_provides_incoming_partition_constraints.AbstractProvidesIncomingPartitionConstraints,spynnaker.pyNN.models.abstract_models.abstract_population_initializable.AbstractPopulationInitializable,spynnaker.pyNN.models.abstract_models.abstract_population_settable.AbstractPopulationSettable,spinn_front_end_common.abstract_models.abstract_changable_after_run.AbstractChangableAfterRun,spinn_front_end_common.abstract_models.abstract_rewrites_data_specification.AbstractRewritesDataSpecification,spynnaker.pyNN.models.abstract_models.abstract_read_parameters_before_set.AbstractReadParametersBeforeSet,spynnaker.pyNN.models.abstract_models.abstract_accepts_incoming_synapses.AbstractAcceptsIncomingSynapses,spinn_front_end_common.abstract_models.impl.provides_key_to_atom_mapping_impl.ProvidesKeyToAtomMappingImpl,spinn_front_end_common.abstract_models.abstract_can_reset.AbstractCanResetUnderlying vertex model for Neural Populations.
-
BASIC_MALLOC_USAGE= 2¶
-
BYTES_TILL_START_OF_GLOBAL_PARAMETERS= 32¶
-
RUNTIME_SDP_PORT_SIZE= 4¶
-
SPIKE_RECORDING_REGION= 0¶
-
TRAFFIC_IDENTIFIER= 'BufferTraffic'¶
-
add_pre_run_connection_holder(connection_holder, edge, synapse_info)[source]¶ Add a connection holder to the vertex to be filled in when the connections are actually generated.
-
clear_recording(variable, buffer_manager, placements, graph_mapper)[source]¶ Clear the recorded data from the object
Parameters: - buffer_manager – the buffer manager object
- placements – the placements object
- graph_mapper – the graph mapper object
Return type: None
-
clear_spike_recording(buffer_manager, placements, graph_mapper)[source]¶ Clear the recorded data from the object
Parameters: - buffer_manager – the buffer manager object
- placements – the placements object
- graph_mapper – the graph mapper object
Return type: None
-
conductance_based¶
-
create_machine_vertex(vertex_slice, resources_required, label=None, constraints=None)[source]¶ Create a machine vertex from this application vertex
Parameters: - vertex_slice (Slice) – The slice of atoms that the machine vertex will cover
- resources_required (ResourceContainer) – the resources used by the machine vertex
- label (str or None) – human readable label for the machine vertex
- constraints (iterable(AbstractConstraint)) – Constraints to be passed on to the machine vertex
-
describe()[source]¶ Get a human-readable description of the cell or synapse type.
The output may be customised by specifying a different template together with an associated template engine (see
pyNN.descriptions).If template is None, then a dictionary containing the template context will be returned.
-
generate_data_specification(spec, placement, machine_time_step, time_scale_factor, graph_mapper, application_graph, machine_graph, routing_info, data_n_time_steps)[source]¶ Generate a data specification.
Parameters: - spec (DataSpecificationGenerator) – The data specification to write to
- placement (Placement) – the placement the vertex is located at
Return type: None
-
get_binary_start_type()[source]¶ Get the start type of the binary to be run.
Return type: ExecutableType
-
get_connections_from_machine(transceiver, placement, edge, graph_mapper, routing_infos, synapse_information, machine_time_step, using_extra_monitor_cores, placements=None, monitor_api=None, monitor_placement=None, monitor_cores=None, handle_time_out_configuration=True, fixed_routes=None)[source]¶ Get the connections from the machine post-run.
-
get_data(variable, n_machine_time_steps, placements, graph_mapper, buffer_manager, machine_time_step)[source]¶ Get the recorded data
Parameters: - variable –
- n_machine_time_steps –
- placements –
- graph_mapper –
- buffer_manager –
- machine_time_step –
Returns:
-
get_incoming_partition_constraints(partition)[source]¶ Get constraints to be added to the given edge that goes in to a vertex of this vertex.
Parameters: - partition (AbstractOutgoingEdgePartition) – An partition that goes in to this vertex
- partition – partition that goes into this vertex
Returns: A list of constraints
Return type: list(AbstractConstraint) Gets the constraints for partitions going into this vertex.
Returns: list of constraints
-
get_initial_value(variable, selector=None)[source]¶ Gets the value for any variable whose in initialize_parameters.keys
Should return the current value not the default one.
Must support the variable as listed in initialize_parameters.keys, ideally also with _init removed or added.
Parameters: - variable (str) – variable name with our without _init
- selector – a description of the subrange to accept. Or None for all. See: _selector_to_ids in SpiNNUtils.spinn_utilities.ranged.abstract_sized.py
Returns: A list or an Object which act like a list
-
get_maximum_delay_supported_in_ms(machine_time_step)[source]¶ Get the maximum delay supported by this vertex.
-
get_neuron_sampling_interval(variable)[source]¶ Returns the current sampling interval for this variable
Parameters: variable – PyNN name of the variable Returns: Sampling interval in micro seconds
-
get_outgoing_partition_constraints(partition)[source]¶ Get constraints to be added to the given edge that comes out of this vertex.
Parameters: - partition (AbstractOutgoingEdgePartition) – An edge that comes out of this vertex
- partition – the partition that leaves this vertex
Returns: A list of constraints
Return type: list(AbstractConstraint) Gets the constraints for partitions going out of this vertex.
Returns: list of constraints
-
get_recordable_variables()[source]¶ Returns a list of the variables this models is expected to collect
-
get_resources_used_by_atoms(vertex_slice, graph, machine_time_step)[source]¶ Get the separate resource requirements for a range of atoms
Parameters: vertex_slice (Slice) – the low value of atoms to calculate resources from Returns: a Resource container that contains a CPUCyclesPerTickResource, DTCMResource and SDRAMResource Return type: ResourceContainer Raises: None – this method does not raise any known exception
-
get_spikes(placements, graph_mapper, buffer_manager, machine_time_step)[source]¶ Get the recorded spikes from the object
Parameters: - placements – the placements object
- graph_mapper – the graph mapper object
- buffer_manager – the buffer manager object
- machine_time_step – the time step of the simulation
Returns: A numpy array of 2-element arrays of (neuron_id, time) ordered by time
-
get_spikes_sampling_interval()[source]¶ Return the current sampling interval for spikes
Returns: Sampling interval in micro seconds
-
get_synapse_id_by_target(target)[source]¶ Get the ID of a synapse given the name.
Parameters: target (str) – The name of the synapse Return type: int
-
get_units(variable)[source]¶ Get units for a given variable
Parameters: variable – the variable to find units from Returns: the units as a string.
-
initialize(variable, value)[source]¶ Set the initial value of one of the state variables of the neurons in this population.
-
initialize_parameters¶ List the parameters that are initializable.
If “foo” is initializable there should be a setter initialize_foo and a getter property foo_init
Returns: list of property names
-
is_recording(variable)[source]¶ Determines if variable is being recorded
Returns: True if variable are being recorded, False otherwise Return type: bool
-
is_recording_spikes()[source]¶ Determine if spikes are being recorded
Returns: True if spikes are being recorded, False otherwise Return type: bool
-
mark_no_changes()[source]¶ Marks the point after which changes are reported, so that new changes can be detected before the next check.
-
n_atoms¶ The number of atoms in the vertex
Return type: int
-
read_parameters_from_machine(transceiver, placement, vertex_slice)[source]¶ Read the parameters from the machine before any are changed
Parameters: - transceiver – the SpinnMan interface
- placement – the placement of a vertex
- vertex_slice – the slice of atoms for this vertex
-
regenerate_data_specification(spec, placement, machine_time_step, time_scale_factor, graph_mapper, routing_info)[source]¶ Regenerate the data specification, only generating regions that have changed and need to be reloaded
Parameters: - spec (DataSpecificationGenerator) – Where to write the regenerated spec
- placement (Placement) – Where are we regenerating for?
-
requires_data_generation¶ True if changes that have been made require that data generation be performed. By default this returns False but can be overridden to indicate changes that require data regeneration.
Return type: bool
-
requires_mapping¶ True if changes that have been made require that mapping be performed. By default this returns False but can be overridden to indicate changes that require mapping.
Return type: bool
-
requires_memory_regions_to_be_reloaded()[source]¶ Return true if any data region needs to be reloaded
Return type: bool
-
ring_buffer_sigma¶
-
set_initial_value(variable, value, selector=None)[source]¶ Sets the value for any variable whose in initialize_parameters.keys
Must support the variable as listed in initialize_parameters.keys, ideally also with _init removed or added
Parameters: - variable (str) – variable name with our without _init
- value – New value for the variable
- selector – a description of the subrange to accept. Or None for all. See: _selector_to_ids in SpiNNUtils.spinn_utilities.ranged.abstract_sized.py
Returns: A list or an Object which act like a list
-
set_recording(variable, new_state=True, sampling_interval=None, indexes=None)[source]¶ Sets variable to being recorded
-
set_recording_spikes(new_state=True, sampling_interval=None, indexes=None)[source]¶ Set spikes to being recorded. If new_state is false all other parameters are ignored.
Parameters: - new_state (bool) – Set if the spikes are recording or not
- sampling_interval – The interval at which spikes are recorded. Must be a whole multiple of the timestep None will be taken as the timestep
- indexes – The indexes of the neurons that will record spikes. If None the assumption is all neurons are recording
-
set_value(key, value)[source]¶ Set a property
Parameters: - key – the name of the parameter to change
- value – the new value of the parameter to assign Set a property of the overall model.
-
spikes_per_second¶
-
synapse_dynamics¶
-
weight_scale¶
-
-
class
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model.AbstractPyNNNeuronModel(model)[source]¶ Bases:
spynnaker.pyNN.models.abstract_pynn_model.AbstractPyNNModel-
create_vertex(n_neurons, label, constraints, spikes_per_second, ring_buffer_sigma, incoming_spike_buffer_size)[source]¶ Create a vertex for a population of the model
Parameters: - n_neurons (int) – The number of neurons in the population
- label (str) – The label to give to the vertex
- constraints (list or None) – A list of constraints to give to the vertex, or None
Returns: An application vertex for the population
Return type:
-
default_population_parameters= {'incoming_spike_buffer_size': None, 'ring_buffer_sigma': None, 'spikes_per_second': None}¶
-
-
class
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard(model_name, binary, neuron_model, input_type, synapse_type, threshold_type, additional_input_type=None)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model.AbstractPyNNNeuronModel
-
class
spynnaker.pyNN.models.neuron.connection_holder.ConnectionHolder(data_items_to_return, as_list, n_pre_atoms, n_post_atoms, connections=None, fixed_values=None, notify=None)[source]¶ Bases:
objectHolds a set of connections to be returned in a PyNN-specific format
Parameters: - data_items_to_return – A list of data fields to be returned
- as_list – True if the data will be returned as a list, False if it is to be returned as a matrix (or series of matrices)
- n_pre_atoms – The number of atoms in the pre-vertex
- n_post_atoms – The number of atoms in the post-vertex
- connections – Any initial connections, as a numpy structured array of source, target, weight and delay
- fixed_values – A list of tuples of field names and fixed values to be appended to the other fields per connection, formatted as [(field_name, value), …]. Note that if the field is to be returned, the name must also appear in data_items_to_return, which determines the order of items in the result
- notify – A callback to call when the connections have all been added. This should accept a single parameter, which will contain the data requested
-
add_connections(connections)[source]¶ Add connections to the holder to be returned
Parameters: connections – The connection to add, as a numpy structured array of source, target, weight and delay
-
connections¶ The connections stored
-
class
spynnaker.pyNN.models.neuron.generator_data.GeneratorData(synaptic_matrix_offset, delayed_synaptic_matrix_offset, max_row_n_words, max_delayed_row_n_words, max_row_n_synapses, max_delayed_row_n_synapses, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_information, max_stage, machine_time_step)[source]¶ Bases:
objectData for each connection of the synapse generator.
-
BASE_SIZE= 68¶
-
gen_data¶ Get the data to be written for this connection
Return type: numpy array of uint32
-
size¶ The size of the generated data in bytes
Return type: int
-
-
class
spynnaker.pyNN.models.neuron.population_machine_vertex.PopulationMachineVertex(resources_required, recorded_region_ids, label, constraints)[source]¶ Bases:
pacman.model.graphs.machine.machine_vertex.MachineVertex,spinn_front_end_common.interface.buffer_management.buffer_models.abstract_receive_buffers_to_host.AbstractReceiveBuffersToHost,spinn_front_end_common.interface.provenance.provides_provenance_data_from_machine_impl.ProvidesProvenanceDataFromMachineImpl,spinn_front_end_common.abstract_models.abstract_recordable.AbstractRecordable,spinn_front_end_common.interface.profiling.abstract_has_profile_data.AbstractHasProfileDataParameters: - resources_required –
- recorded_region_ids –
- label –
- constraints –
-
class
EXTRA_PROVENANCE_DATA_ENTRIES¶ Bases:
enum.EnumAn enumeration.
-
BUFFER_OVERFLOW_COUNT= 2¶
-
CURRENT_TIMER_TIC= 3¶
-
PLASTIC_SYNAPTIC_WEIGHT_SATURATION_COUNT= 4¶
-
PRE_SYNAPTIC_EVENT_COUNT= 0¶
-
SATURATION_COUNT= 1¶
-
-
N_ADDITIONAL_PROVENANCE_DATA_ITEMS= 5¶
-
PROFILE_TAG_LABELS= {0: 'TIMER', 1: 'DMA_READ', 2: 'INCOMING_SPIKE', 3: 'PROCESS_FIXED_SYNAPSES', 4: 'PROCESS_PLASTIC_SYNAPSES'}¶
-
get_profile_data(transceiver, placement)[source]¶ Get the profile data recorded during simulation
Return type: spinn_front_end_common.interface.profiling.profile_data.ProfileData
-
get_provenance_data_from_machine(transceiver, placement)[source]¶ Retrieve the provenance data.
Parameters: - transceiver (Transceiver) – How to talk to the machine
- placement (Placement) – Which vertex are we retrieving from, and where was it
Return type: list(ProvenanceDataItem)
-
get_recorded_region_ids()[source]¶ Get the recording region IDs that have been recorded using buffering
Returns: The region numbers that have active recording Return type: iterable(int)
-
get_recording_region_base_address(txrx, placement)[source]¶ Get the recording region base address
Parameters: - txrx (Transceiver) – the SpiNNMan instance
- placement (Placement) – the placement object of the core to find the address of
Returns: the base address of the recording region
Return type: int
-
resources_required¶ The resources required by the vertex
Return type: ResourceContainer
-
class
spynnaker.pyNN.models.neuron.synaptic_manager.SynapticManager(n_synapse_types, ring_buffer_sigma, spikes_per_second, config, population_table_type=None, synapse_io=None)[source]¶ Bases:
objectDeals with synapses
-
get_connections_from_machine(transceiver, placement, machine_edge, graph_mapper, routing_infos, synapse_info, machine_time_step, using_extra_monitor_cores, placements=None, monitor_api=None, monitor_placement=None, monitor_cores=None, handle_time_out_configuration=True, fixed_routes=None)[source]¶
-
ring_buffer_sigma¶
-
spikes_per_second¶
-
synapse_dynamics¶
-
vertex_executable_suffix¶
-
-
class
spynnaker.pyNN.models.neuron.AbstractPopulationVertex(n_neurons, label, constraints, max_atoms_per_core, spikes_per_second, ring_buffer_sigma, incoming_spike_buffer_size, neuron_impl, pynn_model)[source]¶ Bases:
pacman.model.graphs.application.application_vertex.ApplicationVertex,spinn_front_end_common.abstract_models.abstract_generates_data_specification.AbstractGeneratesDataSpecification,spinn_front_end_common.abstract_models.abstract_has_associated_binary.AbstractHasAssociatedBinary,spynnaker.pyNN.models.abstract_models.abstract_contains_units.AbstractContainsUnits,spynnaker.pyNN.models.common.abstract_spike_recordable.AbstractSpikeRecordable,spynnaker.pyNN.models.common.abstract_neuron_recordable.AbstractNeuronRecordable,spinn_front_end_common.abstract_models.abstract_provides_outgoing_partition_constraints.AbstractProvidesOutgoingPartitionConstraints,spinn_front_end_common.abstract_models.abstract_provides_incoming_partition_constraints.AbstractProvidesIncomingPartitionConstraints,spynnaker.pyNN.models.abstract_models.abstract_population_initializable.AbstractPopulationInitializable,spynnaker.pyNN.models.abstract_models.abstract_population_settable.AbstractPopulationSettable,spinn_front_end_common.abstract_models.abstract_changable_after_run.AbstractChangableAfterRun,spinn_front_end_common.abstract_models.abstract_rewrites_data_specification.AbstractRewritesDataSpecification,spynnaker.pyNN.models.abstract_models.abstract_read_parameters_before_set.AbstractReadParametersBeforeSet,spynnaker.pyNN.models.abstract_models.abstract_accepts_incoming_synapses.AbstractAcceptsIncomingSynapses,spinn_front_end_common.abstract_models.impl.provides_key_to_atom_mapping_impl.ProvidesKeyToAtomMappingImpl,spinn_front_end_common.abstract_models.abstract_can_reset.AbstractCanResetUnderlying vertex model for Neural Populations.
-
BASIC_MALLOC_USAGE= 2¶
-
BYTES_TILL_START_OF_GLOBAL_PARAMETERS= 32¶
-
RUNTIME_SDP_PORT_SIZE= 4¶
-
SPIKE_RECORDING_REGION= 0¶
-
TRAFFIC_IDENTIFIER= 'BufferTraffic'¶
-
add_pre_run_connection_holder(connection_holder, edge, synapse_info)[source]¶ Add a connection holder to the vertex to be filled in when the connections are actually generated.
-
clear_recording(variable, buffer_manager, placements, graph_mapper)[source]¶ Clear the recorded data from the object
Parameters: - buffer_manager – the buffer manager object
- placements – the placements object
- graph_mapper – the graph mapper object
Return type: None
-
clear_spike_recording(buffer_manager, placements, graph_mapper)[source]¶ Clear the recorded data from the object
Parameters: - buffer_manager – the buffer manager object
- placements – the placements object
- graph_mapper – the graph mapper object
Return type: None
-
conductance_based¶
-
create_machine_vertex(vertex_slice, resources_required, label=None, constraints=None)[source]¶ Create a machine vertex from this application vertex
Parameters: - vertex_slice (Slice) – The slice of atoms that the machine vertex will cover
- resources_required (ResourceContainer) – the resources used by the machine vertex
- label (str or None) – human readable label for the machine vertex
- constraints (iterable(AbstractConstraint)) – Constraints to be passed on to the machine vertex
-
describe()[source]¶ Get a human-readable description of the cell or synapse type.
The output may be customised by specifying a different template together with an associated template engine (see
pyNN.descriptions).If template is None, then a dictionary containing the template context will be returned.
-
generate_data_specification(spec, placement, machine_time_step, time_scale_factor, graph_mapper, application_graph, machine_graph, routing_info, data_n_time_steps)[source]¶ Generate a data specification.
Parameters: - spec (DataSpecificationGenerator) – The data specification to write to
- placement (Placement) – the placement the vertex is located at
Return type: None
-
get_binary_start_type()[source]¶ Get the start type of the binary to be run.
Return type: ExecutableType
-
get_connections_from_machine(transceiver, placement, edge, graph_mapper, routing_infos, synapse_information, machine_time_step, using_extra_monitor_cores, placements=None, monitor_api=None, monitor_placement=None, monitor_cores=None, handle_time_out_configuration=True, fixed_routes=None)[source]¶ Get the connections from the machine post-run.
-
get_data(variable, n_machine_time_steps, placements, graph_mapper, buffer_manager, machine_time_step)[source]¶ Get the recorded data
Parameters: - variable –
- n_machine_time_steps –
- placements –
- graph_mapper –
- buffer_manager –
- machine_time_step –
Returns:
-
get_incoming_partition_constraints(partition)[source]¶ Get constraints to be added to the given edge that goes in to a vertex of this vertex.
Parameters: - partition (AbstractOutgoingEdgePartition) – An partition that goes in to this vertex
- partition – partition that goes into this vertex
Returns: A list of constraints
Return type: list(AbstractConstraint) Gets the constraints for partitions going into this vertex.
Returns: list of constraints
-
get_initial_value(variable, selector=None)[source]¶ Gets the value for any variable whose in initialize_parameters.keys
Should return the current value not the default one.
Must support the variable as listed in initialize_parameters.keys, ideally also with _init removed or added.
Parameters: - variable (str) – variable name with our without _init
- selector – a description of the subrange to accept. Or None for all. See: _selector_to_ids in SpiNNUtils.spinn_utilities.ranged.abstract_sized.py
Returns: A list or an Object which act like a list
-
get_maximum_delay_supported_in_ms(machine_time_step)[source]¶ Get the maximum delay supported by this vertex.
-
get_neuron_sampling_interval(variable)[source]¶ Returns the current sampling interval for this variable
Parameters: variable – PyNN name of the variable Returns: Sampling interval in micro seconds
-
get_outgoing_partition_constraints(partition)[source]¶ Get constraints to be added to the given edge that comes out of this vertex.
Parameters: - partition (AbstractOutgoingEdgePartition) – An edge that comes out of this vertex
- partition – the partition that leaves this vertex
Returns: A list of constraints
Return type: list(AbstractConstraint) Gets the constraints for partitions going out of this vertex.
Returns: list of constraints
-
get_recordable_variables()[source]¶ Returns a list of the variables this models is expected to collect
-
get_resources_used_by_atoms(vertex_slice, graph, machine_time_step)[source]¶ Get the separate resource requirements for a range of atoms
Parameters: vertex_slice (Slice) – the low value of atoms to calculate resources from Returns: a Resource container that contains a CPUCyclesPerTickResource, DTCMResource and SDRAMResource Return type: ResourceContainer Raises: None – this method does not raise any known exception
-
get_spikes(placements, graph_mapper, buffer_manager, machine_time_step)[source]¶ Get the recorded spikes from the object
Parameters: - placements – the placements object
- graph_mapper – the graph mapper object
- buffer_manager – the buffer manager object
- machine_time_step – the time step of the simulation
Returns: A numpy array of 2-element arrays of (neuron_id, time) ordered by time
-
get_spikes_sampling_interval()[source]¶ Return the current sampling interval for spikes
Returns: Sampling interval in micro seconds
-
get_synapse_id_by_target(target)[source]¶ Get the ID of a synapse given the name.
Parameters: target (str) – The name of the synapse Return type: int
-
get_units(variable)[source]¶ Get units for a given variable
Parameters: variable – the variable to find units from Returns: the units as a string.
-
initialize(variable, value)[source]¶ Set the initial value of one of the state variables of the neurons in this population.
-
initialize_parameters¶ List the parameters that are initializable.
If “foo” is initializable there should be a setter initialize_foo and a getter property foo_init
Returns: list of property names
-
is_recording(variable)[source]¶ Determines if variable is being recorded
Returns: True if variable are being recorded, False otherwise Return type: bool
-
is_recording_spikes()[source]¶ Determine if spikes are being recorded
Returns: True if spikes are being recorded, False otherwise Return type: bool
-
mark_no_changes()[source]¶ Marks the point after which changes are reported, so that new changes can be detected before the next check.
-
n_atoms¶ The number of atoms in the vertex
Return type: int
-
read_parameters_from_machine(transceiver, placement, vertex_slice)[source]¶ Read the parameters from the machine before any are changed
Parameters: - transceiver – the SpinnMan interface
- placement – the placement of a vertex
- vertex_slice – the slice of atoms for this vertex
-
regenerate_data_specification(spec, placement, machine_time_step, time_scale_factor, graph_mapper, routing_info)[source]¶ Regenerate the data specification, only generating regions that have changed and need to be reloaded
Parameters: - spec (DataSpecificationGenerator) – Where to write the regenerated spec
- placement (Placement) – Where are we regenerating for?
-
requires_data_generation¶ True if changes that have been made require that data generation be performed. By default this returns False but can be overridden to indicate changes that require data regeneration.
Return type: bool
-
requires_mapping¶ True if changes that have been made require that mapping be performed. By default this returns False but can be overridden to indicate changes that require mapping.
Return type: bool
-
requires_memory_regions_to_be_reloaded()[source]¶ Return true if any data region needs to be reloaded
Return type: bool
-
ring_buffer_sigma¶
-
set_initial_value(variable, value, selector=None)[source]¶ Sets the value for any variable whose in initialize_parameters.keys
Must support the variable as listed in initialize_parameters.keys, ideally also with _init removed or added
Parameters: - variable (str) – variable name with our without _init
- value – New value for the variable
- selector – a description of the subrange to accept. Or None for all. See: _selector_to_ids in SpiNNUtils.spinn_utilities.ranged.abstract_sized.py
Returns: A list or an Object which act like a list
-
set_recording(variable, new_state=True, sampling_interval=None, indexes=None)[source]¶ Sets variable to being recorded
-
set_recording_spikes(new_state=True, sampling_interval=None, indexes=None)[source]¶ Set spikes to being recorded. If new_state is false all other parameters are ignored.
Parameters: - new_state (bool) – Set if the spikes are recording or not
- sampling_interval – The interval at which spikes are recorded. Must be a whole multiple of the timestep None will be taken as the timestep
- indexes – The indexes of the neurons that will record spikes. If None the assumption is all neurons are recording
-
set_value(key, value)[source]¶ Set a property
Parameters: - key – the name of the parameter to change
- value – the new value of the parameter to assign Set a property of the overall model.
-
spikes_per_second¶
-
synapse_dynamics¶
-
weight_scale¶
-
-
class
spynnaker.pyNN.models.neuron.ConnectionHolder(data_items_to_return, as_list, n_pre_atoms, n_post_atoms, connections=None, fixed_values=None, notify=None)[source]¶ Bases:
objectHolds a set of connections to be returned in a PyNN-specific format
Parameters: - data_items_to_return – A list of data fields to be returned
- as_list – True if the data will be returned as a list, False if it is to be returned as a matrix (or series of matrices)
- n_pre_atoms – The number of atoms in the pre-vertex
- n_post_atoms – The number of atoms in the post-vertex
- connections – Any initial connections, as a numpy structured array of source, target, weight and delay
- fixed_values – A list of tuples of field names and fixed values to be appended to the other fields per connection, formatted as [(field_name, value), …]. Note that if the field is to be returned, the name must also appear in data_items_to_return, which determines the order of items in the result
- notify – A callback to call when the connections have all been added. This should accept a single parameter, which will contain the data requested
-
add_connections(connections)[source]¶ Add connections to the holder to be returned
Parameters: connections – The connection to add, as a numpy structured array of source, target, weight and delay
-
connections¶ The connections stored
-
class
spynnaker.pyNN.models.neuron.SynapticManager(n_synapse_types, ring_buffer_sigma, spikes_per_second, config, population_table_type=None, synapse_io=None)[source]¶ Bases:
objectDeals with synapses
-
get_connections_from_machine(transceiver, placement, machine_edge, graph_mapper, routing_infos, synapse_info, machine_time_step, using_extra_monitor_cores, placements=None, monitor_api=None, monitor_placement=None, monitor_cores=None, handle_time_out_configuration=True, fixed_routes=None)[source]¶
-
ring_buffer_sigma¶
-
spikes_per_second¶
-
synapse_dynamics¶
-
vertex_executable_suffix¶
-
-
class
spynnaker.pyNN.models.neuron.PopulationMachineVertex(resources_required, recorded_region_ids, label, constraints)[source]¶ Bases:
pacman.model.graphs.machine.machine_vertex.MachineVertex,spinn_front_end_common.interface.buffer_management.buffer_models.abstract_receive_buffers_to_host.AbstractReceiveBuffersToHost,spinn_front_end_common.interface.provenance.provides_provenance_data_from_machine_impl.ProvidesProvenanceDataFromMachineImpl,spinn_front_end_common.abstract_models.abstract_recordable.AbstractRecordable,spinn_front_end_common.interface.profiling.abstract_has_profile_data.AbstractHasProfileDataParameters: - resources_required –
- recorded_region_ids –
- label –
- constraints –
-
class
EXTRA_PROVENANCE_DATA_ENTRIES¶ Bases:
enum.EnumAn enumeration.
-
BUFFER_OVERFLOW_COUNT= 2¶
-
CURRENT_TIMER_TIC= 3¶
-
PLASTIC_SYNAPTIC_WEIGHT_SATURATION_COUNT= 4¶
-
PRE_SYNAPTIC_EVENT_COUNT= 0¶
-
SATURATION_COUNT= 1¶
-
-
N_ADDITIONAL_PROVENANCE_DATA_ITEMS= 5¶
-
PROFILE_TAG_LABELS= {0: 'TIMER', 1: 'DMA_READ', 2: 'INCOMING_SPIKE', 3: 'PROCESS_FIXED_SYNAPSES', 4: 'PROCESS_PLASTIC_SYNAPSES'}¶
-
get_profile_data(transceiver, placement)[source]¶ Get the profile data recorded during simulation
Return type: spinn_front_end_common.interface.profiling.profile_data.ProfileData
-
get_provenance_data_from_machine(transceiver, placement)[source]¶ Retrieve the provenance data.
Parameters: - transceiver (Transceiver) – How to talk to the machine
- placement (Placement) – Which vertex are we retrieving from, and where was it
Return type: list(ProvenanceDataItem)
-
get_recorded_region_ids()[source]¶ Get the recording region IDs that have been recorded using buffering
Returns: The region numbers that have active recording Return type: iterable(int)
-
get_recording_region_base_address(txrx, placement)[source]¶ Get the recording region base address
Parameters: - txrx (Transceiver) – the SpiNNMan instance
- placement (Placement) – the placement object of the core to find the address of
Returns: the base address of the recording region
Return type: int
-
resources_required¶ The resources required by the vertex
Return type: ResourceContainer
-
class
spynnaker.pyNN.models.neuron.AbstractPyNNNeuronModel(model)[source]¶ Bases:
spynnaker.pyNN.models.abstract_pynn_model.AbstractPyNNModel-
create_vertex(n_neurons, label, constraints, spikes_per_second, ring_buffer_sigma, incoming_spike_buffer_size)[source]¶ Create a vertex for a population of the model
Parameters: - n_neurons (int) – The number of neurons in the population
- label (str) – The label to give to the vertex
- constraints (list or None) – A list of constraints to give to the vertex, or None
Returns: An application vertex for the population
Return type:
-
default_population_parameters= {'incoming_spike_buffer_size': None, 'ring_buffer_sigma': None, 'spikes_per_second': None}¶
-
-
class
spynnaker.pyNN.models.neuron.AbstractPyNNNeuronModelStandard(model_name, binary, neuron_model, input_type, synapse_type, threshold_type, additional_input_type=None)[source]¶ Bases:
spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model.AbstractPyNNNeuronModel
-
class
spynnaker.pyNN.models.spike_source.spike_source_array.SpikeSourceArray(spike_times=[])[source]¶ Bases:
spynnaker.pyNN.models.abstract_pynn_model.AbstractPyNNModel-
create_vertex(n_neurons, label, constraints)[source]¶ Create a vertex for a population of the model
Parameters: - n_neurons (int) – The number of neurons in the population
- label (str) – The label to give to the vertex
- constraints (list or None) – A list of constraints to give to the vertex, or None
Returns: An application vertex for the population
Return type:
-
default_population_parameters= {}¶
-
-
class
spynnaker.pyNN.models.spike_source.spike_source_array_vertex.SpikeSourceArrayVertex(n_neurons, spike_times, constraints, label, max_atoms_per_core, model)[source]¶ Bases:
spinn_front_end_common.utility_models.reverse_ip_tag_multi_cast_source.ReverseIpTagMultiCastSource,spynnaker.pyNN.models.common.abstract_spike_recordable.AbstractSpikeRecordable,spynnaker.pyNN.models.common.simple_population_settable.SimplePopulationSettable,spinn_front_end_common.abstract_models.abstract_changable_after_run.AbstractChangableAfterRun,spinn_front_end_common.abstract_models.impl.provides_key_to_atom_mapping_impl.ProvidesKeyToAtomMappingImplModel for play back of spikes
-
SPIKE_RECORDING_REGION_ID= 0¶
-
clear_spike_recording(buffer_manager, placements, graph_mapper)[source]¶ Clear the recorded data from the object
Parameters: - buffer_manager – the buffer manager object
- placements – the placements object
- graph_mapper – the graph mapper object
Return type: None
-
describe()[source]¶ Returns a human-readable description of the cell or synapse type.
The output may be customised by specifying a different template together with an associated template engine (see
pyNN.descriptions).If template is None, then a dictionary containing the template context will be returned.
-
get_spikes(placements, graph_mapper, buffer_manager, machine_time_step)[source]¶ Get the recorded spikes from the object
Parameters: - placements – the placements object
- graph_mapper – the graph mapper object
- buffer_manager – the buffer manager object
- machine_time_step – the time step of the simulation
Returns: A numpy array of 2-element arrays of (neuron_id, time) ordered by time
-
get_spikes_sampling_interval()[source]¶ Return the current sampling interval for spikes
Returns: Sampling interval in micro seconds
-
is_recording_spikes()[source]¶ Determine if spikes are being recorded
Returns: True if spikes are being recorded, False otherwise Return type: bool
-
mark_no_changes()[source]¶ Marks the point after which changes are reported, so that new changes can be detected before the next check.
-
requires_mapping¶ True if changes that have been made require that mapping be performed. By default this returns False but can be overridden to indicate changes that require mapping.
Return type: bool
-
set_recording_spikes(new_state=True, sampling_interval=None, indexes=None)[source]¶ Set spikes to being recorded. If new_state is false all other parameters are ignored.
Parameters: - new_state (bool) – Set if the spikes are recording or not
- sampling_interval – The interval at which spikes are recorded. Must be a whole multiple of the timestep None will be taken as the timestep
- indexes – The indexes of the neurons that will record spikes. If None the assumption is all neurons are recording
-
spike_times¶ The spike times of the spike source array
-
-
class
spynnaker.pyNN.models.spike_source.spike_source_from_file.SpikeSourceFromFile(spike_time_file, min_atom=None, max_atom=None, min_time=None, max_time=None, split_value='t')[source]¶ Bases:
spynnaker.pyNN.models.spike_source.spike_source_array.SpikeSourceArraySpikeSourceArray that works from a file
-
spike_times¶
-
-
class
spynnaker.pyNN.models.spike_source.spike_source_poisson.SpikeSourcePoisson(rate=1.0, start=0, duration=None)[source]¶ Bases:
spynnaker.pyNN.models.abstract_pynn_model.AbstractPyNNModel-
create_vertex(n_neurons, label, constraints, seed, max_rate)[source]¶ Create a vertex for a population of the model
Parameters: - n_neurons (int) – The number of neurons in the population
- label (str) – The label to give to the vertex
- constraints (list or None) – A list of constraints to give to the vertex, or None
Returns: An application vertex for the population
Return type:
-
default_population_parameters= {'max_rate': None, 'seed': None}¶
-
-
class
spynnaker.pyNN.models.spike_source.spike_source_poisson_machine_vertex.SpikeSourcePoissonMachineVertex(resources_required, is_recording, constraints=None, label=None)[source]¶ Bases:
pacman.model.graphs.machine.machine_vertex.MachineVertex,spinn_front_end_common.interface.buffer_management.buffer_models.abstract_receive_buffers_to_host.AbstractReceiveBuffersToHost,spinn_front_end_common.interface.provenance.provides_provenance_data_from_machine_impl.ProvidesProvenanceDataFromMachineImpl,spinn_front_end_common.abstract_models.abstract_recordable.AbstractRecordable,spinn_front_end_common.abstract_models.abstract_supports_database_injection.AbstractSupportsDatabaseInjection,spinn_front_end_common.interface.profiling.abstract_has_profile_data.AbstractHasProfileData-
class
POISSON_SPIKE_SOURCE_REGIONS¶ Bases:
enum.EnumAn enumeration.
-
POISSON_PARAMS_REGION= 1¶
-
PROFILER_REGION= 4¶
-
PROVENANCE_REGION= 3¶
-
SPIKE_HISTORY_REGION= 2¶
-
SYSTEM_REGION= 0¶
-
-
PROFILE_TAG_LABELS= {0: 'TIMER', 1: 'PROB_FUNC'}¶
-
get_profile_data(transceiver, placement)[source]¶ Get the profile data recorded during simulation
Return type: spinn_front_end_common.interface.profiling.profile_data.ProfileData
-
get_recorded_region_ids()[source]¶ Get the recording region IDs that have been recorded using buffering
Returns: The region numbers that have active recording Return type: iterable(int)
-
get_recording_region_base_address(txrx, placement)[source]¶ Get the recording region base address
Parameters: - txrx (Transceiver) – the SpiNNMan instance
- placement (Placement) – the placement object of the core to find the address of
Returns: the base address of the recording region
Return type: int
-
is_in_injection_mode(graph)[source]¶ Whether this vertex is actually in injection mode.
Return type: bool
-
resources_required¶ The resources required by the vertex
Return type: ResourceContainer
-
class
-
class
spynnaker.pyNN.models.spike_source.spike_source_poisson_vertex.SpikeSourcePoissonVertex(n_neurons, constraints, label, rate, max_rate, start, duration, seed, max_atoms_per_core, model)[source]¶ Bases:
pacman.model.graphs.application.application_vertex.ApplicationVertex,spinn_front_end_common.abstract_models.abstract_generates_data_specification.AbstractGeneratesDataSpecification,spinn_front_end_common.abstract_models.abstract_has_associated_binary.AbstractHasAssociatedBinary,spynnaker.pyNN.models.common.abstract_spike_recordable.AbstractSpikeRecordable,spinn_front_end_common.abstract_models.abstract_provides_outgoing_partition_constraints.AbstractProvidesOutgoingPartitionConstraints,spinn_front_end_common.abstract_models.abstract_changable_after_run.AbstractChangableAfterRun,spynnaker.pyNN.models.abstract_models.abstract_read_parameters_before_set.AbstractReadParametersBeforeSet,spinn_front_end_common.abstract_models.abstract_rewrites_data_specification.AbstractRewritesDataSpecification,spynnaker.pyNN.models.common.simple_population_settable.SimplePopulationSettable,spinn_front_end_common.abstract_models.impl.provides_key_to_atom_mapping_impl.ProvidesKeyToAtomMappingImplA Poisson Spike source object
-
SPIKE_RECORDING_REGION_ID= 0¶
-
clear_spike_recording(buffer_manager, placements, graph_mapper)[source]¶ Clear the recorded data from the object
Parameters: - buffer_manager – the buffer manager object
- placements – the placements object
- graph_mapper – the graph mapper object
Return type: None
-
create_machine_vertex(vertex_slice, resources_required, label=None, constraints=None)[source]¶ Create a machine vertex from this application vertex
Parameters: - vertex_slice (Slice) – The slice of atoms that the machine vertex will cover
- resources_required (ResourceContainer) – the resources used by the machine vertex
- label (str or None) – human readable label for the machine vertex
- constraints (iterable(AbstractConstraint)) – Constraints to be passed on to the machine vertex
-
describe()[source]¶ Returns a human-readable description of the cell or synapse type.
The output may be customised by specifying a different template together with an associated template engine (see
pyNN.descriptions).If template is None, then a dictionary containing the template context will be returned.
-
duration¶
-
generate_data_specification(spec, placement, machine_time_step, time_scale_factor, graph_mapper, routing_info, data_n_time_steps, graph)[source]¶ Generate a data specification.
Parameters: - spec (DataSpecificationGenerator) – The data specification to write to
- placement (Placement) – the placement the vertex is located at
Return type: None
-
get_binary_start_type()[source]¶ Get the start type of the binary to be run.
Return type: ExecutableType
-
get_outgoing_partition_constraints(partition)[source]¶ Get constraints to be added to the given edge that comes out of this vertex.
Parameters: partition (AbstractOutgoingEdgePartition) – An edge that comes out of this vertex Returns: A list of constraints Return type: list(AbstractConstraint)
-
static
get_params_bytes(vertex_slice)[source]¶ Gets the size of the poisson parameters in bytes
Parameters: vertex_slice –
-
get_resources_used_by_atoms(vertex_slice, machine_time_step)[source]¶ Get the separate resource requirements for a range of atoms
Parameters: vertex_slice (Slice) – the low value of atoms to calculate resources from Returns: a Resource container that contains a CPUCyclesPerTickResource, DTCMResource and SDRAMResource Return type: ResourceContainer Raises: None – this method does not raise any known exception
-
get_spikes(placements, graph_mapper, buffer_manager, machine_time_step)[source]¶ Get the recorded spikes from the object
Parameters: - placements – the placements object
- graph_mapper – the graph mapper object
- buffer_manager – the buffer manager object
- machine_time_step – the time step of the simulation
Returns: A numpy array of 2-element arrays of (neuron_id, time) ordered by time
-
get_spikes_sampling_interval()[source]¶ Return the current sampling interval for spikes
Returns: Sampling interval in micro seconds
-
is_recording_spikes()[source]¶ Determine if spikes are being recorded
Returns: True if spikes are being recorded, False otherwise Return type: bool
-
mark_no_changes()[source]¶ Marks the point after which changes are reported, so that new changes can be detected before the next check.
-
max_rate¶
-
n_atoms¶ The number of atoms in the vertex
Return type: int
-
rate¶
-
read_parameters_from_machine(transceiver, placement, vertex_slice)[source]¶ Read the parameters from the machine before any are changed
Parameters: - transceiver – the SpinnMan interface
- placement – the placement of a vertex
- vertex_slice – the slice of atoms for this vertex
-
regenerate_data_specification(spec, placement, machine_time_step, time_scale_factor, graph_mapper, routing_info, graph)[source]¶ Regenerate the data specification, only generating regions that have changed and need to be reloaded
Parameters: - spec (DataSpecificationGenerator) – Where to write the regenerated spec
- placement (Placement) – Where are we regenerating for?
-
requires_mapping¶ True if changes that have been made require that mapping be performed. By default this returns False but can be overridden to indicate changes that require mapping.
Return type: bool
-
requires_memory_regions_to_be_reloaded()[source]¶ Return true if any data region needs to be reloaded
Return type: bool
-
reserve_memory_regions(spec, placement, graph_mapper)[source]¶ Reserve memory regions for poisson source parameters and output buffer.
Parameters: - spec – the data specification writer
- placement – the location this vertex resides on in the machine
- graph_mapper – the mapping between app and machine graphs
Returns: None
-
seed¶
-
set_recording_spikes(new_state=True, sampling_interval=None, indexes=None)[source]¶ Set spikes to being recorded. If new_state is false all other parameters are ignored.
Parameters: - new_state (bool) – Set if the spikes are recording or not
- sampling_interval – The interval at which spikes are recorded. Must be a whole multiple of the timestep None will be taken as the timestep
- indexes – The indexes of the neurons that will record spikes. If None the assumption is all neurons are recording
-
set_value(key, value)[source]¶ Set a property
Parameters: - key – the name of the parameter to change
- value – the new value of the parameter to assign
-
start¶
-
-
class
spynnaker.pyNN.models.spike_source.SpikeSourceArray(spike_times=[])[source]¶ Bases:
spynnaker.pyNN.models.abstract_pynn_model.AbstractPyNNModel-
create_vertex(n_neurons, label, constraints)[source]¶ Create a vertex for a population of the model
Parameters: - n_neurons (int) – The number of neurons in the population
- label (str) – The label to give to the vertex
- constraints (list or None) – A list of constraints to give to the vertex, or None
Returns: An application vertex for the population
Return type:
-
default_population_parameters= {}¶
-
-
class
spynnaker.pyNN.models.spike_source.SpikeSourceFromFile(spike_time_file, min_atom=None, max_atom=None, min_time=None, max_time=None, split_value='t')[source]¶ Bases:
spynnaker.pyNN.models.spike_source.spike_source_array.SpikeSourceArraySpikeSourceArray that works from a file
-
spike_times¶
-
-
class
spynnaker.pyNN.models.spike_source.SpikeSourcePoisson(rate=1.0, start=0, duration=None)[source]¶ Bases:
spynnaker.pyNN.models.abstract_pynn_model.AbstractPyNNModel-
create_vertex(n_neurons, label, constraints, seed, max_rate)[source]¶ Create a vertex for a population of the model
Parameters: - n_neurons (int) – The number of neurons in the population
- label (str) – The label to give to the vertex
- constraints (list or None) – A list of constraints to give to the vertex, or None
Returns: An application vertex for the population
Return type:
-
default_population_parameters= {'max_rate': None, 'seed': None}¶
-
-
class
spynnaker.pyNN.models.utility_models.delays.delay_extension_machine_vertex.DelayExtensionMachineVertex(resources_required, label, constraints=None)[source]¶ Bases:
pacman.model.graphs.machine.machine_vertex.MachineVertex,spinn_front_end_common.interface.provenance.provides_provenance_data_from_machine_impl.ProvidesProvenanceDataFromMachineImpl-
class
EXTRA_PROVENANCE_DATA_ENTRIES¶ Bases:
enum.EnumAn enumeration.
-
N_BUFFER_OVERFLOWS= 4¶
-
N_DELAYS= 5¶
-
N_PACKETS_ADDED= 2¶
-
N_PACKETS_PROCESSED= 1¶
-
N_PACKETS_RECEIVED= 0¶
-
N_PACKETS_SENT= 3¶
-
-
N_EXTRA_PROVENANCE_DATA_ENTRIES= 6¶
-
get_provenance_data_from_machine(transceiver, placement)[source]¶ Retrieve the provenance data.
Parameters: - transceiver (Transceiver) – How to talk to the machine
- placement (Placement) – Which vertex are we retrieving from, and where was it
Return type: list(ProvenanceDataItem)
-
resources_required¶ The resources required by the vertex
Return type: ResourceContainer
-
class
-
class
spynnaker.pyNN.models.utility_models.delays.delay_extension_vertex.DelayExtensionVertex(n_neurons, delay_per_stage, source_vertex, machine_time_step, timescale_factor, constraints=None, label='DelayExtension')[source]¶ Bases:
pacman.model.graphs.application.application_vertex.ApplicationVertex,spinn_front_end_common.abstract_models.abstract_generates_data_specification.AbstractGeneratesDataSpecification,spinn_front_end_common.abstract_models.abstract_has_associated_binary.AbstractHasAssociatedBinary,spinn_front_end_common.abstract_models.abstract_provides_outgoing_partition_constraints.AbstractProvidesOutgoingPartitionConstraints,spinn_front_end_common.abstract_models.abstract_provides_n_keys_for_partition.AbstractProvidesNKeysForPartitionProvide delays to incoming spikes in multiples of the maximum delays of a neuron (typically 16 or 32)
Parameters: - n_neurons – the number of neurons
- delay_per_stage – the delay per stage
- source_vertex – where messages are coming from
- machine_time_step – how long is the machine time step
- timescale_factor – what slowdown factor has been applied
- constraints – the vertex constraints
- label – the vertex label
-
add_delays(vertex_slice, source_ids, stages)[source]¶ Add delayed connections for a given vertex slice
-
add_generator_data(max_row_n_synapses, max_delayed_row_n_synapses, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_information, max_stage, machine_time_step)[source]¶ Add delays for a connection to be generated
-
create_machine_vertex(vertex_slice, resources_required, label=None, constraints=None)[source]¶ Create a machine vertex from this application vertex
Parameters: - vertex_slice (Slice) – The slice of atoms that the machine vertex will cover
- resources_required (ResourceContainer) – the resources used by the machine vertex
- label (str or None) – human readable label for the machine vertex
- constraints (iterable(AbstractConstraint)) – Constraints to be passed on to the machine vertex
-
gen_on_machine(vertex_slice)[source]¶ Determine if the given slice needs to be generated on the machine
-
generate_data_specification(spec, placement, machine_graph, graph_mapper, routing_infos)[source]¶ Generate a data specification.
Parameters: - spec (DataSpecificationGenerator) – The data specification to write to
- placement (Placement) – the placement the vertex is located at
Return type: None
-
get_binary_start_type()[source]¶ Get the start type of the binary to be run.
Return type: ExecutableType
-
get_n_keys_for_partition(partition, graph_mapper)[source]¶ Get the number of keys required by the given partition of edges.
Parameters: - partition (AbstractOutgoingEdgePartition) – An partition that comes out of this vertex
- graph_mapper (
GraphMapper) – A mapper between the graphs
Returns: A list of constraints
Return type: list(AbstractConstraint)
-
get_outgoing_partition_constraints(partition)[source]¶ Get constraints to be added to the given edge that comes out of this vertex.
Parameters: partition (AbstractOutgoingEdgePartition) – An edge that comes out of this vertex Returns: A list of constraints Return type: list(AbstractConstraint)
-
get_resources_used_by_atoms(vertex_slice, graph)[source]¶ Get the separate resource requirements for a range of atoms
Parameters: vertex_slice (Slice) – the low value of atoms to calculate resources from Returns: a Resource container that contains a CPUCyclesPerTickResource, DTCMResource and SDRAMResource Return type: ResourceContainer Raises: None – this method does not raise any known exception
-
n_atoms¶ The number of atoms in the vertex
Return type: int
-
n_delay_stages¶ The maximum number of delay stages required by any connection out of this delay extension vertex
-
source_vertex¶
-
class
spynnaker.pyNN.models.utility_models.delays.delay_generator_data.DelayGeneratorData(max_row_n_synapses, max_delayed_row_n_synapses, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_information, max_stage, machine_time_step)[source]¶ Bases:
objectData for each connection of the delay generator
-
BASE_SIZE= 32¶
-
gen_data¶ Get the data to be written for this connection
Return type: numpy array of uint32
-
size¶ The size of the generated data in bytes
Return type: int
-
-
class
spynnaker.pyNN.models.utility_models.delays.DelayBlock(n_delay_stages, delay_per_stage, vertex_slice)[source]¶ Bases:
objectA block of delays for a vertex.
-
delay_block¶
-
-
class
spynnaker.pyNN.models.utility_models.delays.DelayExtensionMachineVertex(resources_required, label, constraints=None)[source]¶ Bases:
pacman.model.graphs.machine.machine_vertex.MachineVertex,spinn_front_end_common.interface.provenance.provides_provenance_data_from_machine_impl.ProvidesProvenanceDataFromMachineImpl-
class
EXTRA_PROVENANCE_DATA_ENTRIES¶ Bases:
enum.EnumAn enumeration.
-
N_BUFFER_OVERFLOWS= 4¶
-
N_DELAYS= 5¶
-
N_PACKETS_ADDED= 2¶
-
N_PACKETS_PROCESSED= 1¶
-
N_PACKETS_RECEIVED= 0¶
-
N_PACKETS_SENT= 3¶
-
-
N_EXTRA_PROVENANCE_DATA_ENTRIES= 6¶
-
get_provenance_data_from_machine(transceiver, placement)[source]¶ Retrieve the provenance data.
Parameters: - transceiver (Transceiver) – How to talk to the machine
- placement (Placement) – Which vertex are we retrieving from, and where was it
Return type: list(ProvenanceDataItem)
-
resources_required¶ The resources required by the vertex
Return type: ResourceContainer
-
class
-
class
spynnaker.pyNN.models.utility_models.delays.DelayExtensionVertex(n_neurons, delay_per_stage, source_vertex, machine_time_step, timescale_factor, constraints=None, label='DelayExtension')[source]¶ Bases:
pacman.model.graphs.application.application_vertex.ApplicationVertex,spinn_front_end_common.abstract_models.abstract_generates_data_specification.AbstractGeneratesDataSpecification,spinn_front_end_common.abstract_models.abstract_has_associated_binary.AbstractHasAssociatedBinary,spinn_front_end_common.abstract_models.abstract_provides_outgoing_partition_constraints.AbstractProvidesOutgoingPartitionConstraints,spinn_front_end_common.abstract_models.abstract_provides_n_keys_for_partition.AbstractProvidesNKeysForPartitionProvide delays to incoming spikes in multiples of the maximum delays of a neuron (typically 16 or 32)
Parameters: - n_neurons – the number of neurons
- delay_per_stage – the delay per stage
- source_vertex – where messages are coming from
- machine_time_step – how long is the machine time step
- timescale_factor – what slowdown factor has been applied
- constraints – the vertex constraints
- label – the vertex label
-
add_delays(vertex_slice, source_ids, stages)[source]¶ Add delayed connections for a given vertex slice
-
add_generator_data(max_row_n_synapses, max_delayed_row_n_synapses, pre_slices, pre_slice_index, post_slices, post_slice_index, pre_vertex_slice, post_vertex_slice, synapse_information, max_stage, machine_time_step)[source]¶ Add delays for a connection to be generated
-
create_machine_vertex(vertex_slice, resources_required, label=None, constraints=None)[source]¶ Create a machine vertex from this application vertex
Parameters: - vertex_slice (Slice) – The slice of atoms that the machine vertex will cover
- resources_required (ResourceContainer) – the resources used by the machine vertex
- label (str or None) – human readable label for the machine vertex
- constraints (iterable(AbstractConstraint)) – Constraints to be passed on to the machine vertex
-
gen_on_machine(vertex_slice)[source]¶ Determine if the given slice needs to be generated on the machine
-
generate_data_specification(spec, placement, machine_graph, graph_mapper, routing_infos)[source]¶ Generate a data specification.
Parameters: - spec (DataSpecificationGenerator) – The data specification to write to
- placement (Placement) – the placement the vertex is located at
Return type: None
-
get_binary_start_type()[source]¶ Get the start type of the binary to be run.
Return type: ExecutableType
-
get_n_keys_for_partition(partition, graph_mapper)[source]¶ Get the number of keys required by the given partition of edges.
Parameters: - partition (AbstractOutgoingEdgePartition) – An partition that comes out of this vertex
- graph_mapper (
GraphMapper) – A mapper between the graphs
Returns: A list of constraints
Return type: list(AbstractConstraint)
-
get_outgoing_partition_constraints(partition)[source]¶ Get constraints to be added to the given edge that comes out of this vertex.
Parameters: partition (AbstractOutgoingEdgePartition) – An edge that comes out of this vertex Returns: A list of constraints Return type: list(AbstractConstraint)
-
get_resources_used_by_atoms(vertex_slice, graph)[source]¶ Get the separate resource requirements for a range of atoms
Parameters: vertex_slice (Slice) – the low value of atoms to calculate resources from Returns: a Resource container that contains a CPUCyclesPerTickResource, DTCMResource and SDRAMResource Return type: ResourceContainer Raises: None – this method does not raise any known exception
-
n_atoms¶ The number of atoms in the vertex
Return type: int
-
n_delay_stages¶ The maximum number of delay stages required by any connection out of this delay extension vertex
-
source_vertex¶
-
class
spynnaker.pyNN.models.utility_models.spike_injector.spike_injector.SpikeInjector[source]¶ Bases:
spynnaker.pyNN.models.abstract_pynn_model.AbstractPyNNModel-
create_vertex(n_neurons, label, constraints, port, virtual_key, reserve_reverse_ip_tag)[source]¶ Create a vertex for a population of the model
Parameters: - n_neurons (int) – The number of neurons in the population
- label (str) – The label to give to the vertex
- constraints (list or None) – A list of constraints to give to the vertex, or None
Returns: An application vertex for the population
Return type:
-
default_population_parameters= {'port': None, 'reserve_reverse_ip_tag': False, 'virtual_key': None}¶
-
-
class
spynnaker.pyNN.models.utility_models.spike_injector.spike_injector_vertex.SpikeInjectorVertex(n_neurons, label, constraints, port, virtual_key, reserve_reverse_ip_tag)[source]¶ Bases:
spinn_front_end_common.utility_models.reverse_ip_tag_multi_cast_source.ReverseIpTagMultiCastSource,spinn_front_end_common.abstract_models.abstract_provides_outgoing_partition_constraints.AbstractProvidesOutgoingPartitionConstraints,spynnaker.pyNN.models.common.abstract_spike_recordable.AbstractSpikeRecordable,spynnaker.pyNN.models.common.simple_population_settable.SimplePopulationSettableAn Injector of Spikes for PyNN populations. This only allows the user to specify the virtual_key of the population to identify the population
-
SPIKE_RECORDING_REGION_ID= 0¶
-
clear_spike_recording(buffer_manager, placements, graph_mapper)[source]¶ Clear the recorded data from the object
Parameters: - buffer_manager – the buffer manager object
- placements – the placements object
- graph_mapper – the graph mapper object
Return type: None
-
default_parameters= {'label': 'spikeInjector', 'port': None, 'virtual_key': None}¶
-
describe()[source]¶ Returns a human-readable description of the cell or synapse type.
The output may be customised by specifying a different template together with an associated template engine (see
pyNN.descriptions).If template is None, then a dictionary containing the template context will be returned.
-
get_outgoing_partition_constraints(partition)[source]¶ Get constraints to be added to the given edge that comes out of this vertex.
Parameters: partition (AbstractOutgoingEdgePartition) – An edge that comes out of this vertex Returns: A list of constraints Return type: list(AbstractConstraint)
-
get_spikes(placements, graph_mapper, buffer_manager, machine_time_step)[source]¶ Get the recorded spikes from the object
Parameters: - placements – the placements object
- graph_mapper – the graph mapper object
- buffer_manager – the buffer manager object
- machine_time_step – the time step of the simulation
Returns: A numpy array of 2-element arrays of (neuron_id, time) ordered by time
-
get_spikes_sampling_interval()[source]¶ Return the current sampling interval for spikes
Returns: Sampling interval in micro seconds
-
is_recording_spikes()[source]¶ Determine if spikes are being recorded
Returns: True if spikes are being recorded, False otherwise Return type: bool
-
port¶
-
set_recording_spikes(new_state=True, sampling_interval=None, indexes=None)[source]¶ Set spikes to being recorded. If new_state is false all other parameters are ignored.
Parameters: - new_state (bool) – Set if the spikes are recording or not
- sampling_interval – The interval at which spikes are recorded. Must be a whole multiple of the timestep None will be taken as the timestep
- indexes – The indexes of the neurons that will record spikes. If None the assumption is all neurons are recording
-
virtual_key¶
-
-
class
spynnaker.pyNN.models.utility_models.spike_injector.SpikeInjector[source]¶ Bases:
spynnaker.pyNN.models.abstract_pynn_model.AbstractPyNNModel-
create_vertex(n_neurons, label, constraints, port, virtual_key, reserve_reverse_ip_tag)[source]¶ Create a vertex for a population of the model
Parameters: - n_neurons (int) – The number of neurons in the population
- label (str) – The label to give to the vertex
- constraints (list or None) – A list of constraints to give to the vertex, or None
Returns: An application vertex for the population
Return type:
-
default_population_parameters= {'port': None, 'reserve_reverse_ip_tag': False, 'virtual_key': None}¶
-
-
class
spynnaker.pyNN.models.abstract_pynn_model.AbstractPyNNModel[source]¶ Bases:
objectA Model that can be passed in to a Population object in PyNN
-
create_vertex(n_neurons, label, constraints)[source]¶ Create a vertex for a population of the model
Parameters: - n_neurons (int) – The number of neurons in the population
- label (str) – The label to give to the vertex
- constraints (list or None) – A list of constraints to give to the vertex, or None
Returns: An application vertex for the population
Return type:
-
default_initial_values= {}¶
-
default_parameters= {}¶
-
default_population_parameters¶ - Get the default values for the parameters at the population level
- These are parameters that can be passed in to the Population constructor in addition to the standard PyNN options
Return type: dict(str, object)
-
classmethod
get_max_atoms_per_core()[source]¶ Get the maximum number of atoms per core for this model
Return type: int
-
classmethod
get_parameter_names()[source]¶ Get the names of the parameters of the model
Return type: list(str)
-
-
spynnaker.pyNN.models.defaults.default_initial_values(state_variables)[source]¶ Specifies arguments which are state variables. Only works on the __init__ method of a class that is additionally decorated with
defaults`()Parameters: state_variables (set of str) – The names of the arguments that are state variables
-
spynnaker.pyNN.models.defaults.default_parameters(parameters)[source]¶ Specifies arguments which are parameters. Only works on the __init__ method of a class that is additionally decorated with
defaults`()Parameters: parameters (set of str) – The names of the arguments that are parameters
-
spynnaker.pyNN.models.defaults.defaults(cls)[source]¶ Get the default parameters and state variables from the arguments to the __init__ method. This uses the decorators
default_parameters()anddefault_initial_values()to determine the parameters and state variables respectively. If only one is specified, the other is assumed to be the remaining arguments. If neither are specified, it is assumed that all default arguments are parameters.
-
class
spynnaker.pyNN.models.pynn_population_common.PyNNPopulationCommon(spinnaker_control, size, label, constraints, model, structure, initial_values, additional_parameters=None)[source]¶ Bases:
object-
add_placement_constraint(x, y, p=None)[source]¶ Add a placement constraint
Parameters: - x (int) – The x-coordinate of the placement constraint
- y (int) – The y-coordinate of the placement constraint
- p (int) – The processor ID of the placement constraint (optional)
-
can_record(variable)[source]¶ Determine whether variable can be recorded from this population.
Note: This is supported by sPyNNaker8
-
conductance_based¶ True if the population uses conductance inputs
-
first_id¶
-
get(parameter_names, gather=False)[source]¶ Get the values of a parameter for every local cell in the population.
Parameters: parameter_names – Name of parameter. This is either a single string or a list of strings Returns: A single list of values (or possibly a single value) if paramter_names is a string, or a dict of these if parameter names is a list. Return type: str or list(str) or dict(str,str) or dict(str,list(str))
-
get_by_selector(selector, parameter_names)[source]¶ Get the values of a parameter for the selected cell in the population.
Parameters: - parameter_names – Name of parameter. This is either a single string or a list of strings
- selector – a description of the subrange to accept. Or None for all. See: _selector_to_ids in SpiNNUtils.spinn_utilities.ranged.abstract_sized.py
Returns: A single list of values (or possibly a single value) if paramter_names is a string or a dict of these if parameter names is a list.
Return type: str or list(str) or dict(str,str) or dict(str,list(str))
-
id_to_index(id)[source]¶ Given the ID(s) of cell(s) in the Population, return its (their) index (order in the Population).
-
id_to_local_index(cell_id)[source]¶ Given the ID(s) of cell(s) in the Population, return its (their) index (order in the Population), counting only cells on the local MPI node.
-
index_to_id(index)[source]¶ Given the index (order in the Population) of cell(s) in the Population, return their ID(s)
-
label¶ The label of the population
-
last_id¶
-
local_size¶ The number of local cells
-
positions¶ Return the position array for structured populations.
-
requires_mapping¶
-
set(parameter, value=None)[source]¶ Set one or more parameters for every cell in the population.
param can be a dict, in which case value should not be supplied, or a string giving the parameter name, in which case value is the parameter value. value can be a numeric value, or list of such (e.g. for setting spike times):
p.set("tau_m", 20.0). p.set({'tau_m':20, 'v_rest':-65})
Parameters: - parameter (str or dict) – the parameter to set
- value – the value of the parameter to set.
-
set_by_selector(selector, parameter, value=None)[source]¶ Set one or more parameters for selected cell in the population.
param can be a dict, in which case value should not be supplied, or a string giving the parameter name, in which case value is the parameter value. value can be a numeric value, or list of such (e.g. for setting spike times):
p.set("tau_m", 20.0). p.set({'tau_m':20, 'v_rest':-65})
Parameters: - selector – See RangedList.set_value_by_selector as this is just a pass through method
- parameter – the parameter to set
- value – the value of the parameter to set.
-
set_constraint(constraint)[source]¶ Apply a constraint to a population that restricts the processor onto which its atoms will be placed.
-
set_mapping_constraint(constraint_dict)[source]¶ Add a placement constraint - for backwards compatibility
Parameters: constraint_dict (dict(str, int)) – A dictionary containing “x”, “y” and optionally “p” as keys, and ints as values
-
set_max_atoms_per_core(max_atoms_per_core)[source]¶ Supports the setting of this population’s max atoms per core
Parameters: max_atoms_per_core – the new value for the max atoms per core.
-
size¶ The number of neurons in the population
-
structure¶ Return the structure for the population.
-
-
class
spynnaker.pyNN.models.pynn_projection_common.PyNNProjectionCommon(spinnaker_control, connector, synapse_dynamics_stdp, target, pre_synaptic_population, post_synaptic_population, rng, machine_time_step, user_max_delay, label, time_scale_factor)[source]¶ Bases:
objectA container for all the connections of a given type (same synapse type and plasticity mechanisms) between two populations, together with methods to set parameters of those connections, including of plasticity mechanisms.
-
requires_mapping¶
-
spynnaker.pyNN.overridden_pacman_functions package¶
spynnaker.pyNN.protocols package¶
-
spynnaker.pyNN.protocols.munich_io_spinnaker_link_protocol.GET_RETINA_PAYLOAD_VALUE(payload)[source]¶
-
class
spynnaker.pyNN.protocols.munich_io_spinnaker_link_protocol.MunichIoSpiNNakerLinkProtocol(mode, instance_key=None, uart_id=0)[source]¶ Bases:
objectProvides Multicast commands for the Munich SpiNNaker-Link protocol
Parameters: - mode – The mode of operation of the protocol
- instance_key – The optional instance key to use
- uart_id – The ID of the UART when needed
-
class
MODES¶ Bases:
enum.EnumAn enumeration.
-
BALL_BALANCER= 3¶
-
FREE= 5¶
-
MY_ORO_BOTICS= 4¶
-
PUSH_BOT= 1¶
-
RESET_TO_DEFAULT= 0¶
-
SPOMNIBOT= 2¶
-
-
add_payload_logic_to_current_output_key¶
-
bias_values_key¶
-
configure_master_key_key¶
-
disable_retina_key¶
-
enable_disable_motor_key¶
-
generic_motor0_raw_output_leak_to_0_key¶
-
generic_motor0_raw_output_permanent_key¶
-
generic_motor1_raw_output_leak_to_0_key¶
-
generic_motor1_raw_output_permanent_key¶
-
generic_motor_total_period_key¶
-
instance_key¶ The key of this instance of the protocol
-
master_slave_key¶
-
mode¶
-
poll_individual_sensor_continuously_key¶
-
poll_sensors_once_key¶
-
protocol_instance= 0¶
-
push_bot_laser_config_active_time_key¶
-
push_bot_laser_config_total_period_key¶
-
push_bot_laser_set_frequency_key¶
-
push_bot_led_back_active_time_key¶
-
push_bot_led_front_active_time_key¶
-
push_bot_led_set_frequency_key¶
-
push_bot_led_total_period_key¶
-
push_bot_motor_0_leaking_towards_zero_key¶
-
push_bot_motor_0_permanent_key¶
-
push_bot_motor_1_leaking_towards_zero_key¶
-
push_bot_motor_1_permanent_key¶
-
push_bot_speaker_config_active_time_key¶
-
push_bot_speaker_config_total_period_key¶
-
push_bot_speaker_set_melody_key¶
-
push_bot_speaker_set_tone_key¶
-
pwm_pin_output_timer_a_channel_0_ratio_key¶
-
pwm_pin_output_timer_a_channel_1_ratio_key¶
-
pwm_pin_output_timer_a_duration_key¶
-
pwm_pin_output_timer_b_channel_1_ratio_key¶
-
pwm_pin_output_timer_b_duration_key¶
-
pwm_pin_output_timer_c_channel_0_ratio_key¶
-
pwm_pin_output_timer_c_duration_key¶
-
query_state_of_io_lines_key¶
-
remove_payload_logic_to_current_output_key¶
-
reset_retina_key¶
-
static
sent_mode_command()[source]¶ True if the mode command has ever been requested by any instance
-
set_mode_key¶
-
set_output_pattern_for_payload_key¶
-
set_payload_pins_to_high_impedance_key¶
-
set_retina_key_key¶
-
set_retina_transmission(retina_key=<RetinaKey.NATIVE_128_X_128: 67108864>, retina_payload=None, time=None)[source]¶ Set the retina transmission key
Parameters: - retina_key – the new key for the retina
- retina_payload (enum or None) – the new payload for the set retina key command packet
- time – when to transmit this packet
Returns: the command to send
Return type: spinn_front_end_common.utility_models.multi_cast_command.MultiCastCommand
-
set_retina_transmission_key¶
-
turn_off_sensor_reporting_key¶
-
uart_id¶
-
class
spynnaker.pyNN.protocols.munich_io_spinnaker_link_protocol.RetinaKey(value, pixels, bits_per_coordinate)[source]¶ Bases:
enum.EnumAn enumeration.
-
DOWNSAMPLE_16_X_16= 268435456¶
-
DOWNSAMPLE_32_X_32= 201326592¶
-
DOWNSAMPLE_64_X_64= 134217728¶
-
FIXED_KEY= 0¶
-
NATIVE_128_X_128= 67108864¶
-
bits_per_coordinate¶
-
n_neurons¶
-
pixels¶
-
-
class
spynnaker.pyNN.protocols.munich_io_spinnaker_link_protocol.RetinaPayload(value, n_payload_bytes)[source]¶ Bases:
enum.EnumAn enumeration.
-
ABSOLUTE_2_BYTE_TIMESTAMPS= 1073741824¶
-
ABSOLUTE_3_BYTE_TIMESTAMPS= 1610612736¶
-
ABSOLUTE_4_BYTE_TIMESTAMPS= 2147483648¶
-
DELTA_TIMESTAMPS= 536870912¶
-
EVENTS_IN_PAYLOAD= 0¶
-
NO_PAYLOAD= 0¶
-
n_payload_bytes¶
-
-
class
spynnaker.pyNN.protocols.MunichIoSpiNNakerLinkProtocol(mode, instance_key=None, uart_id=0)[source]¶ Bases:
objectProvides Multicast commands for the Munich SpiNNaker-Link protocol
Parameters: - mode – The mode of operation of the protocol
- instance_key – The optional instance key to use
- uart_id – The ID of the UART when needed
-
class
MODES¶ Bases:
enum.EnumAn enumeration.
-
BALL_BALANCER= 3¶
-
FREE= 5¶
-
MY_ORO_BOTICS= 4¶
-
PUSH_BOT= 1¶
-
RESET_TO_DEFAULT= 0¶
-
SPOMNIBOT= 2¶
-
-
add_payload_logic_to_current_output_key¶
-
bias_values_key¶
-
configure_master_key_key¶
-
disable_retina_key¶
-
enable_disable_motor_key¶
-
generic_motor0_raw_output_leak_to_0_key¶
-
generic_motor0_raw_output_permanent_key¶
-
generic_motor1_raw_output_leak_to_0_key¶
-
generic_motor1_raw_output_permanent_key¶
-
generic_motor_total_period_key¶
-
instance_key¶ The key of this instance of the protocol
-
master_slave_key¶
-
mode¶
-
poll_individual_sensor_continuously_key¶
-
poll_sensors_once_key¶
-
protocol_instance= 0¶
-
push_bot_laser_config_active_time_key¶
-
push_bot_laser_config_total_period_key¶
-
push_bot_laser_set_frequency_key¶
-
push_bot_led_back_active_time_key¶
-
push_bot_led_front_active_time_key¶
-
push_bot_led_set_frequency_key¶
-
push_bot_led_total_period_key¶
-
push_bot_motor_0_leaking_towards_zero_key¶
-
push_bot_motor_0_permanent_key¶
-
push_bot_motor_1_leaking_towards_zero_key¶
-
push_bot_motor_1_permanent_key¶
-
push_bot_speaker_config_active_time_key¶
-
push_bot_speaker_config_total_period_key¶
-
push_bot_speaker_set_melody_key¶
-
push_bot_speaker_set_tone_key¶
-
pwm_pin_output_timer_a_channel_0_ratio_key¶
-
pwm_pin_output_timer_a_channel_1_ratio_key¶
-
pwm_pin_output_timer_a_duration_key¶
-
pwm_pin_output_timer_b_channel_1_ratio_key¶
-
pwm_pin_output_timer_b_duration_key¶
-
pwm_pin_output_timer_c_channel_0_ratio_key¶
-
pwm_pin_output_timer_c_duration_key¶
-
query_state_of_io_lines_key¶
-
remove_payload_logic_to_current_output_key¶
-
reset_retina_key¶
-
static
sent_mode_command()[source]¶ True if the mode command has ever been requested by any instance
-
set_mode_key¶
-
set_output_pattern_for_payload_key¶
-
set_payload_pins_to_high_impedance_key¶
-
set_retina_key_key¶
-
set_retina_transmission(retina_key=<RetinaKey.NATIVE_128_X_128: 67108864>, retina_payload=None, time=None)[source]¶ Set the retina transmission key
Parameters: - retina_key – the new key for the retina
- retina_payload (enum or None) – the new payload for the set retina key command packet
- time – when to transmit this packet
Returns: the command to send
Return type: spinn_front_end_common.utility_models.multi_cast_command.MultiCastCommand
-
set_retina_transmission_key¶
-
turn_off_sensor_reporting_key¶
-
uart_id¶
-
class
spynnaker.pyNN.protocols.RetinaKey(value, pixels, bits_per_coordinate)[source]¶ Bases:
enum.EnumAn enumeration.
-
DOWNSAMPLE_16_X_16= 268435456¶
-
DOWNSAMPLE_32_X_32= 201326592¶
-
DOWNSAMPLE_64_X_64= 134217728¶
-
FIXED_KEY= 0¶
-
NATIVE_128_X_128= 67108864¶
-
bits_per_coordinate¶
-
n_neurons¶
-
pixels¶
-
-
class
spynnaker.pyNN.protocols.RetinaPayload(value, n_payload_bytes)[source]¶ Bases:
enum.EnumAn enumeration.
-
ABSOLUTE_2_BYTE_TIMESTAMPS= 1073741824¶
-
ABSOLUTE_3_BYTE_TIMESTAMPS= 1610612736¶
-
ABSOLUTE_4_BYTE_TIMESTAMPS= 2147483648¶
-
DELTA_TIMESTAMPS= 536870912¶
-
EVENTS_IN_PAYLOAD= 0¶
-
NO_PAYLOAD= 0¶
-
n_payload_bytes¶
-
spynnaker.pyNN.utilities package¶
-
class
spynnaker.pyNN.utilities.random_stats.abstract_random_stats.AbstractRandomStats[source]¶ Bases:
objectStatistics about PyNN RandomDistribution objects
-
high(dist)[source]¶ Return the high cutoff value of the distribution, or None if the distribution is unbounded
-
-
class
spynnaker.pyNN.utilities.random_stats.AbstractRandomStats[source]¶ Bases:
objectStatistics about PyNN RandomDistribution objects
-
high(dist)[source]¶ Return the high cutoff value of the distribution, or None if the distribution is unbounded
-
-
class
spynnaker.pyNN.utilities.ranged.spynnaker_ranged_dict.SpynnakerRangeDictionary(size, defaults=None)[source]¶ Bases:
spinn_utilities.ranged.range_dictionary.RangeDictionaryThe Object is set up initially where every ID in the range will share the same value for each key. All keys must be of type str. The default Values can be anything including None.
Parameters: - size (int) – Fixed number of IDs / Length of lists
- defaults (dict) – Default dictionary where all keys must be str
-
list_factory(size, value, key)[source]¶ Defines which class or subclass of RangedList to use
Main purpose is for subclasses to use a subclass or RangedList All parameters are pass through ones to the List constructor
Parameters: - size – Fixed length of the list
- value – value to given to all elements in the list
- key – The dict key this list covers.
Returns: AbstractList in this case a RangedList
-
class
spynnaker.pyNN.utilities.ranged.spynnaker_ranged_list.SpynnakerRangedList(size=None, value=None, key=None, use_list_as_value=False)[source]¶ Bases:
spinn_utilities.ranged.ranged_list.RangedListParameters: - size – Fixed length of the list
- value – value to given to all elements in the list
- key – The dict key this list covers. This is used only for better Exception messages
- use_list_as_value – True if the value is a list
-
static
as_list(value, size, ids=None)[source]¶ Converts (if required) the value into a list of a given size. An exception is raised if value cannot be given size elements.
Note
This method can be extended to add other conversions to list in which case
is_list()must also be extended.Parameters: value – Returns: value as a list Raises: Exception – if the number of values and the size do not match
-
class
spynnaker.pyNN.utilities.ranged.SpynnakerRangeDictionary(size, defaults=None)[source]¶ Bases:
spinn_utilities.ranged.range_dictionary.RangeDictionaryThe Object is set up initially where every ID in the range will share the same value for each key. All keys must be of type str. The default Values can be anything including None.
Parameters: - size (int) – Fixed number of IDs / Length of lists
- defaults (dict) – Default dictionary where all keys must be str
-
list_factory(size, value, key)[source]¶ Defines which class or subclass of RangedList to use
Main purpose is for subclasses to use a subclass or RangedList All parameters are pass through ones to the List constructor
Parameters: - size – Fixed length of the list
- value – value to given to all elements in the list
- key – The dict key this list covers.
Returns: AbstractList in this case a RangedList
-
class
spynnaker.pyNN.utilities.ranged.SpynnakerRangedList(size=None, value=None, key=None, use_list_as_value=False)[source]¶ Bases:
spinn_utilities.ranged.ranged_list.RangedListParameters: - size – Fixed length of the list
- value – value to given to all elements in the list
- key – The dict key this list covers. This is used only for better Exception messages
- use_list_as_value – True if the value is a list
-
static
as_list(value, size, ids=None)[source]¶ Converts (if required) the value into a list of a given size. An exception is raised if value cannot be given size elements.
Note
This method can be extended to add other conversions to list in which case
is_list()must also be extended.Parameters: value – Returns: value as a list Raises: Exception – if the number of values and the size do not match
-
class
spynnaker.pyNN.utilities.constants.POPULATION_BASED_REGIONS¶ Bases:
enum.EnumAn enumeration.
-
CONNECTOR_BUILDER= 9¶
-
DIRECT_MATRIX= 10¶
-
NEURON_PARAMS= 1¶
-
POPULATION_TABLE= 3¶
-
PROFILING= 8¶
-
PROVENANCE_DATA= 7¶
-
RECORDING= 6¶
-
SYNAPSE_DYNAMICS= 5¶
-
SYNAPSE_PARAMS= 2¶
-
SYNAPTIC_MATRIX= 4¶
-
SYSTEM= 0¶
-
-
class
spynnaker.pyNN.utilities.extracted_data.ExtractedData[source]¶ Bases:
objectData holder for all synaptic data being extracted in parallel. @Chimp: play here to hearts content.
-
class
spynnaker.pyNN.utilities.spynnaker_failed_state.SpynnakerFailedState[source]¶ Bases:
spynnaker.pyNN.spynnaker_simulator_interface.SpynnakerSimulatorInterface,spinn_front_end_common.utilities.failed_state.FailedState,object-
has_reset_last¶
-
max_delay¶
-
min_delay¶
-
utility class containing simple helper methods
-
spynnaker.pyNN.utilities.utility_calls.check_directory_exists_and_create_if_not(filename)[source]¶ Create a parent directory for a file if it doesn’t exist
Parameters: filename – The file whose parent directory is to be created
-
spynnaker.pyNN.utilities.utility_calls.convert_param_to_numpy(param, no_atoms)[source]¶ Convert parameters into numpy arrays
Parameters: - param – the param to convert
- no_atoms – the number of atoms available for conversion of param
Return numpy.array: the converted param in whatever format it was given
-
spynnaker.pyNN.utilities.utility_calls.convert_to(value, data_type)[source]¶ Convert a value to a given data type
Parameters: - value – The value to convert
- data_type – The data type to convert to
Returns: The converted data as a numpy data type
-
spynnaker.pyNN.utilities.utility_calls.get_maximum_probable_value(dist, n_items, chance=0.01)[source]¶ Get the likely maximum value of a RandomDistribution given a number of draws
-
spynnaker.pyNN.utilities.utility_calls.get_minimum_probable_value(dist, n_items, chance=0.01)[source]¶ Get the likely minimum value of a RandomDistribution given a number of draws
-
spynnaker.pyNN.utilities.utility_calls.get_n_bits(n_values)[source]¶ Determine how many bits are required for the given number of values
-
spynnaker.pyNN.utilities.utility_calls.get_probability_within_range(dist, lower, upper)[source]¶ Get the probability that a value will fall within the given range for a given RandomDistribution
-
spynnaker.pyNN.utilities.utility_calls.get_probable_maximum_selected(n_total_trials, n_trials, selection_prob, chance=0.01)[source]¶ Get the likely maximum number of items that will be selected from a set of n_trials from a total set of n_total_trials with a probability of selection of selection_prob
-
spynnaker.pyNN.utilities.utility_calls.get_standard_deviation(dist)[source]¶ Get the standard deviation of a RandomDistribution
-
spynnaker.pyNN.utilities.utility_calls.get_variance(dist)[source]¶ Get the variance of a RandomDistribution
-
spynnaker.pyNN.utilities.utility_calls.high(dist)[source]¶ Gets the high or max boundary value for this distribution
Could return None
-
spynnaker.pyNN.utilities.utility_calls.low(dist)[source]¶ Gets the high or min boundary value for this distribution
Could return None
-
spynnaker.pyNN.utilities.utility_calls.read_in_data_from_file(file_path, min_atom, max_atom, min_time, max_time, extra=False)[source]¶ - Read in a file of data values where the values are in a format of:
- <time> <atom ID> <data value>
Parameters: - file_path – absolute path to a file containing the data
- min_atom – min neuron ID to which neurons to read in
- max_atom – max neuron ID to which neurons to read in
- min_time – min time slot to read neurons values of.
- max_time – max time slot to read neurons values of.
Returns: a numpy array of (time stamp, atom ID, data value)
-
spynnaker.pyNN.utilities.utility_calls.read_spikes_from_file(file_path, min_atom=0, max_atom=inf, min_time=0, max_time=inf, split_value='\t')[source]¶ Read spikes from a file formatted as: <time> <neuron ID>
Parameters: - file_path (str) – absolute path to a file containing spike values
- min_atom (int) – min neuron ID to which neurons to read in
- max_atom (int) – max neuron ID to which neurons to read in
- min_time (int) – min time slot to read neurons values of.
- max_time (int) – max time slot to read neurons values of.
- split_value (str) – the pattern to split by
Returns: a numpy array with max_atom elements each of which is a list of spike times.
Return type: numpy.array(int, int)
Submodules¶
spynnaker.pyNN.abstract_spinnaker_common module¶
-
class
spynnaker.pyNN.abstract_spinnaker_common.AbstractSpiNNakerCommon(graph_label, database_socket_addresses, n_chips_required, timestep, max_delay, min_delay, hostname, user_extra_algorithm_xml_path=None, user_extra_mapping_inputs=None, user_extra_algorithms_pre_run=None, time_scale_factor=None, extra_post_run_algorithms=None, extra_mapping_algorithms=None, extra_load_algorithms=None, front_end_versions=None)[source]¶ Bases:
spinn_front_end_common.interface.abstract_spinnaker_base.AbstractSpinnakerBase,spynnaker.pyNN.spynnaker_simulator_interface.SpynnakerSimulatorInterfaceMain interface for neural code.
-
CONFIG_FILE_NAME= 'spynnaker.cfg'¶
-
add_application_vertex(vertex_to_add)[source]¶ Parameters: vertex – the vertex to add to the graph
Type: ApplicationVertex
Return type: None
Raises: - ConfigurationException – when both graphs contain vertices
- PacmanConfigurationException – If there is an attempt to add the same vertex more than once
-
get_projections_data(projection_to_attribute_map)[source]¶ Common data extractor for projection data. Allows fully exploitation of the ????
Parameters: projection_to_attribute_map (dict of projection with set of attributes) – the projection to attributes mapping Returns: a extracted data object with get method for getting the data Return type: spynnaker.pyNN.utilities.extracted_data.ExtractedData
-
id_counter¶ Getter for id_counter, currently used by the populations.
Note
Maybe it could live in the pop class???
Returns: Return type: int
-
max_delay¶ The maximum supported delay, in milliseconds.
-
min_delay¶ The minimum supported delay, in milliseconds.
-
static
register_binary_search_path(search_path)[source]¶ Register an additional binary search path for executables.
Parameters: search_path – absolute search path for binaries
-
run(run_time)[source]¶ Run the model created.
Parameters: run_time – the time (in milliseconds) to run the simulation for
-
stop(turn_off_machine=None, clear_routing_tables=None, clear_tags=None)[source]¶ Parameters: - turn_off_machine (bool) – decides if the machine should be powered down after running the execution. Note that this powers down all boards connected to the BMP connections given to the transceiver
- clear_routing_tables (bool) – informs the tool chain if it should turn off the clearing of the routing tables
- clear_tags (boolean) – informs the tool chain if it should clear the tags off the machine at stop
Return type: None
-
time_scale_factor¶ The multiplicative scaling from application time to real execution time.
Returns: the time scale factor
-
spynnaker.pyNN.exceptions module¶
-
exception
spynnaker.pyNN.exceptions.DelayExtensionException[source]¶ Bases:
spinn_front_end_common.utilities.exceptions.ConfigurationExceptionRaised when a delay extension vertex fails.
-
exception
spynnaker.pyNN.exceptions.FilterableException[source]¶ Bases:
spynnaker.pyNN.exceptions.SpynnakerExceptionRaised when it is not possible to determine if an edge should be filtered.
-
exception
spynnaker.pyNN.exceptions.InvalidParameterType[source]¶ Bases:
spynnaker.pyNN.exceptions.SpynnakerExceptionRaised when a parameter is not recognised.
-
exception
spynnaker.pyNN.exceptions.MemReadException[source]¶ Bases:
spynnaker.pyNN.exceptions.SpynnakerExceptionRaised when the PyNN front end fails to read a certain memory region.
-
exception
spynnaker.pyNN.exceptions.SpynnakerException[source]¶ Bases:
ExceptionSuperclass of all exceptions from the PyNN module.
-
exception
spynnaker.pyNN.exceptions.SynapseRowTooBigException(max_size, message)[source]¶ Bases:
spynnaker.pyNN.exceptions.SpynnakerExceptionRaised when a synapse row is bigger than is allowed.PyNN
-
max_size¶ The maximum size allowed.
-
-
exception
spynnaker.pyNN.exceptions.SynapticBlockGenerationException[source]¶ Bases:
spinn_front_end_common.utilities.exceptions.ConfigurationExceptionRaised when the synaptic manager fails to generate a synaptic block.
-
exception
spynnaker.pyNN.exceptions.SynapticBlockReadException[source]¶ Bases:
spinn_front_end_common.utilities.exceptions.ConfigurationExceptionRaised when the synaptic manager fails to read a synaptic block or convert it into readable values.
-
exception
spynnaker.pyNN.exceptions.SynapticConfigurationException[source]¶ Bases:
spinn_front_end_common.utilities.exceptions.ConfigurationExceptionRaised when the synaptic manager fails for some reason.
-
exception
spynnaker.pyNN.exceptions.SynapticMaxIncomingAtomsSupportException[source]¶ Bases:
spinn_front_end_common.utilities.exceptions.ConfigurationExceptionRaised when a synaptic sublist exceeds the max atoms possible to be supported.
spynnaker.pyNN.spynnaker_external_device_plugin_manager module¶
-
class
spynnaker.pyNN.spynnaker_external_device_plugin_manager.SpynnakerExternalDevicePluginManager[source]¶ Bases:
objectUser-level interface for the external device plugin manager.
-
static
activate_live_output_for(population, database_notify_host=None, database_notify_port_num=None, database_ack_port_num=None, board_address=None, port=None, host=None, tag=None, strip_sdp=True, use_prefix=False, key_prefix=None, prefix_type=None, message_type=<EIEIOType.KEY_32_BIT: 2>, right_shift=0, payload_as_time_stamps=True, notify=True, use_payload_prefix=True, payload_prefix=None, payload_right_shift=0, number_of_packets_sent_per_time_step=0)[source]¶ Output the spikes from a given population from SpiNNaker as they occur in the simulation.
Parameters: - population (
spynnaker.pyNN.models.pynn_population_common.PyNNPopulationCommon) – The population to activate the live output for - database_notify_host (str) – The hostname for the device which is listening to the database notification.
- database_ack_port_num (int) – The port number to which a external device will acknowledge that they have finished reading the database and are ready for it to start execution
- database_notify_port_num (int) – The port number to which a external device will receive the database is ready command
- board_address (str) – A fixed board address required for the tag, or None if any address is OK
- key_prefix (int or None) – the prefix to be applied to the key
- prefix_type – if the prefix type is 32 bit or 16 bit
- message_type – If the message is a EIEIO command message, or an EIEIO data message with 16 bit or 32 bit keys.
- payload_as_time_stamps –
- right_shift –
- use_payload_prefix –
- notify –
- payload_prefix –
- payload_right_shift –
- number_of_packets_sent_per_time_step –
- port (int) – The UDP port to which the live spikes will be sent. If not specified, the port will be taken from the “live_spike_port” parameter in the “Recording” section of the sPyNNaker configuration file.
- host (str) – The host name or IP address to which the live spikes will be sent. If not specified, the host will be taken from the “live_spike_host” parameter in the “Recording” section of the sPyNNaker configuration file.
- tag (int) – The IP tag to be used for the spikes. If not specified, one will be automatically assigned
- strip_sdp (bool) – Determines if the SDP headers will be stripped from the transmitted packet.
- use_prefix (bool) – Determines if the spike packet will contain a common prefix for the spikes
- population (
-
static
activate_live_output_to(population, device)[source]¶ Activate the output of spikes from a population to an external device. Note that all spikes will be sent to the device.
Parameters: - population (
spynnaker.pyNN.models.pynn_population_common.PyNNPopulationCommon) – The pyNN population object from which spikes will be sent. - device (
spynnaker.pyNN.models.pynn_population_common.PyNNPopulationCommonorpacman.model.graphs.application.ApplicationVertex) – The pyNN population or external device to which the spikes will be sent.
- population (
-
static
add_database_socket_address(database_notify_host, database_notify_port_num, database_ack_port_num)[source]¶
-
static
add_edge(vertex, device_vertex, partition_id)[source]¶ Add an edge between two vertices (often a vertex and a external device) on a given partition.
Parameters: - vertex – the pre vertex to connect the edge from
- device_vertex – the post vertex to connect the edge to
- partition_id – the partition identifier for making nets
Return type: None
-
static
add_poisson_live_rate_control(poisson_population, control_label_extension='_control', receive_port=None, database_notify_host=None, database_notify_port_num=None, database_ack_port_num=None, notify=True, reserve_reverse_ip_tag=False)[source]¶ Add a live rate controller to a Poisson population.
Parameters: - poisson_population (
spynnaker.pyNN.models.pynn_population_common.PyNNPopulationCommon) – The population to control - control_label_extension (str) – An extension to add to the label of the Poisson source. Must match up with the equivalent in the SpynnakerPoissonControlConnection
- receive_port (int) – The port that the SpiNNaker board should listen on
- database_notify_host (str) – the hostname for the device which is listening to the database notification.
- database_ack_port_num (int) – the port number to which a external device will acknowledge that they have finished reading the database and are ready for it to start execution
- database_notify_port_num (int) – The port number to which a external device will receive the database is ready command
- reserve_reverse_ip_tag (bool) – True if a reverse ip tag is to be used, False if SDP is to be used (default)
- poisson_population (
-
static
add_socket_address(socket_address)[source]¶ Add a socket address to the list to be checked by the notification protocol.
Parameters: socket_address – the socket address Return type: None:
-
static
update_live_packet_gather_tracker(vertex_to_record_from, port, hostname, tag=None, board_address=None, strip_sdp=True, use_prefix=False, key_prefix=None, prefix_type=None, message_type=<EIEIOType.KEY_32_BIT: 2>, right_shift=0, payload_as_time_stamps=True, use_payload_prefix=True, payload_prefix=None, payload_right_shift=0, number_of_packets_sent_per_time_step=0, partition_id=None)[source]¶ Add an edge from a vertex to the live packet gatherer, builds as needed and has all the parameters for the creation of the live packet gatherer if needed.
-
static
spynnaker.pyNN.spynnaker_simulator_interface module¶
Module contents¶
Submodules¶
spynnaker.gsyn_tools module¶
spynnaker.plot_utils module¶
spynnaker.spike_checker module¶
-
spynnaker.spike_checker.synfire_multiple_lines_spike_checker(spikes, nNeurons, lines, wrap_around=True)[source]¶ Checks that there are the expected number of spike lines
Parameters: - spikes – The spikes
- nNeurons – Number of neurons
- lines – Expected number of lines
- wrap_around – If True the lines will wrap around when reaching the last neuron