spynnaker.pyNN.models.spike_source package

Submodules

spynnaker.pyNN.models.spike_source.spike_source_array module

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:

pacman.model.graphs.application.ApplicationVertex
default_population_parameters = {}

spynnaker.pyNN.models.spike_source.spike_source_array_vertex module

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.ProvidesKeyToAtomMappingImpl

Model 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

spynnaker.pyNN.models.spike_source.spike_source_from_file module

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.SpikeSourceArray

SpikeSourceArray that works from a file

spike_times

spynnaker.pyNN.models.spike_source.spike_source_poisson module

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:

pacman.model.graphs.application.ApplicationVertex
default_population_parameters = {'max_rate': None, 'seed': None}
classmethod get_max_atoms_per_core()[source]

Get the maximum number of atoms per core for this model

Return type:int
classmethod set_model_max_atoms_per_core(n_atoms=500)[source]

Set the maximum number of atoms per core for this model

Parameters:n_atoms (int or None) – The new maximum, or None for the largest possible

spynnaker.pyNN.models.spike_source.spike_source_poisson_machine_vertex module

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.Enum

An 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
is_recording()[source]

Deduce if the recorder is actually recording

Return type:bool
resources_required

The resources required by the vertex

Return type:ResourceContainer

spynnaker.pyNN.models.spike_source.spike_source_poisson_vertex module

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.ProvidesKeyToAtomMappingImpl

A 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
convert_rate(rate)[source]
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:
Return type:

None
get_binary_file_name()[source]

Get the binary name to be run for this vertex.

Return type:str
get_binary_start_type()[source]

Get the start type of the binary to be run.

Return type:ExecutableType
static get_cpu_usage_for_atoms()[source]
static get_dtcm_usage_for_atoms()[source]
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_recording_sdram_usage(vertex_slice, machine_time_step)[source]
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.

mark_regions_reloaded()[source]

Indicate that the regions have been reloaded

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:
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

Module contents

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:

pacman.model.graphs.application.ApplicationVertex
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.SpikeSourceArray

SpikeSourceArray 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:

pacman.model.graphs.application.ApplicationVertex
default_population_parameters = {'max_rate': None, 'seed': None}
classmethod get_max_atoms_per_core()[source]

Get the maximum number of atoms per core for this model

Return type:int
classmethod set_model_max_atoms_per_core(n_atoms=500)[source]

Set the maximum number of atoms per core for this model

Parameters:n_atoms (int or None) – The new maximum, or None for the largest possible