spynnaker.pyNN.models.utility_models.spike_injector package

Submodules

spynnaker.pyNN.models.utility_models.spike_injector.spike_injector module

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:

pacman.model.graphs.application.ApplicationVertex
default_population_parameters = {'port': None, 'reserve_reverse_ip_tag': False, 'virtual_key': None}

spynnaker.pyNN.models.utility_models.spike_injector.spike_injector_vertex module

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.SimplePopulationSettable

An 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

Module contents

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:

pacman.model.graphs.application.ApplicationVertex
default_population_parameters = {'port': None, 'reserve_reverse_ip_tag': False, 'virtual_key': None}