spynnaker.pyNN.models.spike_source package

Submodules

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, splitter)

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)

Clear the recorded data from the object

Parameters:

• buffer_manager (BufferManager) – the buffer manager object
• placements (Placements) – the placements object

Return type:

None

describe()

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, buffer_manager)

Get the recorded spikes from the object

Parameters:

• placements (Placements) – the placements object
• buffer_manager (BufferManager) – the buffer manager object

Returns:

A numpy array of 2-element arrays of (neuron_id, time) ordered by time, one element per event

Return type:

ndarray(tuple(int,int))

get_spikes_sampling_interval()

Return the current sampling interval for spikes

Returns:Sampling interval in microseconds Return type:float

is_recording_spikes()

Determine if spikes are being recorded

Returns:True if spikes are being recorded, False otherwise Return type:bool

mark_no_changes()

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)

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 (int or None) – The interval at which spikes are recorded. Must be a whole multiple of the timestep. None will be taken as the timestep.
• indexes (list(int) or None) – The indexes of the neurons that will record spikes. If None the assumption is all neurons are recording

set_value_by_selector(selector, key, value)

Sets the value for a particular key but only for the selected subset.

Parameters:

• selector (None or slice or int or list(bool) or list(int)) – See RangedList.set_value_by_selector as this is just a pass through method
• key (str) – the name of the parameter to change
• value (float or int or list(float) or list(int)) – the new value of the parameter to assign

spike_times

The spike times of the spike source array

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, app_vertex=None, vertex_slice=None)

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_supports_database_injection.AbstractSupportsDatabaseInjection, spinn_front_end_common.interface.profiling.abstract_has_profile_data.AbstractHasProfileData, spinn_front_end_common.abstract_models.abstract_has_associated_binary.AbstractHasAssociatedBinary, spinn_front_end_common.abstract_models.abstract_rewrites_data_specification.AbstractRewritesDataSpecification, spinn_front_end_common.abstract_models.abstract_generates_data_specification.AbstractGeneratesDataSpecification, spynnaker.pyNN.models.abstract_models.abstract_read_parameters_before_set.AbstractReadParametersBeforeSet

class EXTRA_PROVENANCE_DATA_ENTRIES

Bases: enum.Enum

Entries for the provenance data generated by standard neuron models.

TDMA_MISSED_SLOTS = 0

The number of pre-synaptic events

FAST_RATE_PER_TICK_CUTOFF = 10

PARAMS_BASE_WORDS = 13

class POISSON_SPIKE_SOURCE_REGIONS

Bases: enum.Enum

An enumeration.

POISSON_PARAMS_REGION = 1

PROFILER_REGION = 5

PROVENANCE_REGION = 4

RATES_REGION = 2

SPIKE_HISTORY_REGION = 3

SYSTEM_REGION = 0

TDMA_REGION = 6

PROFILE_TAG_LABELS = {0: 'TIMER', 1: 'PROB_FUNC'}

SEED_OFFSET_BYTES = 36

SEED_SIZE_BYTES = 16

SLOW_RATE_PER_TICK_CUTOFF = 0.01

generate_data_specification(spec, placement, routing_info, data_n_time_steps, graph, first_machine_time_step)

Generate a data specification.

Parameters:

• spec (DataSpecificationGenerator) – The data specification to write to
• placement (Placement) – The placement the vertex is located at
• routing_info (RoutingInfo) –
• data_n_time_steps (int) –
• graph (MachineGraph) –
• first_machine_time_step (int) –

Return type:

None

get_binary_file_name()

Get the binary name to be run for this vertex.

Return type:str

get_binary_start_type()

Get the start type of the binary to be run.

Return type:ExecutableType

get_profile_data(transceiver, placement)

Get the profile data recorded during simulation

Parameters:

• transceiver (Transceiver) –
• placement (Placement) –

Return type:

ProfileData

get_recorded_region_ids()

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)

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

Whether this vertex is actually in injection mode.

Return type:bool

max_spikes_per_second()

Get maximum expected number of spikes per second

Parameters:variable (str) – the variable to find units from Returns:the units as a string. Return type:str

max_spikes_per_ts()

Get maximum expected number of spikes per timestep

Return type:int

parse_extra_provenance_items(label, names, provenance_data)

Convert the remaining provenance words (those not in the standard set) into provenance items.

Called by get_provenance_data_from_machine()

Parameters:

• label (str) – A descriptive label for the vertex (derived from label and placed position) to be used for provenance error reporting to the user.
• names (list(str)) – The base names describing the location of the machine vertex producing the provenance.
• provenance_data (list(int)) – The list of words of raw provenance data.

Returns:

The interpreted provenance items.

Return type:

iterable(ProvenanceDataItem)

poisson_param_region_address(placement, transceiver)

poisson_rate_region_address(placement, transceiver)

read_parameters_from_machine(transceiver, placement, vertex_slice)

Read the parameters from the machine before any are changed.

Parameters:

• transceiver (Transceiver) – the SpinnMan interface
• placement (Placement) – the placement of a vertex
• vertex_slice (Slice) – the slice of atoms for this vertex

Return type:

None

regenerate_data_specification(spec, placement, routing_info, graph, first_machine_time_step)

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?
• routing_info (RoutingInfo) –
• graph (MachineGraph) –
• first_machine_time_step (int) –

reload_required(first_machine_time_step)

Return true if any data region needs to be reloaded

Return type:bool

reserve_memory_regions(spec, placement)

Reserve memory regions for Poisson source parameters and output buffer.

Parameters:

• spec (DataSpecificationGenerator) – the data specification writer
• placement (Placement) – the location this vertex resides on in the machine

Returns:

None

resources_required

The resources required by the vertex

Return type:ResourceContainer

set_reload_required(new_value)

Indicate that the regions have been reloaded

Parameters:new_value – the new value Return type:None

spynnaker.pyNN.models.spike_source.spike_source_poisson_machine_vertex.get_rates_bytes(vertex_slice, rate_data)

Gets the size of the Poisson rates in bytes

Parameters:vertex_slice (Slice) – Return type:int

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, seed, max_atoms_per_core, model, rate=None, start=None, duration=None, rates=None, starts=None, durations=None, max_rate=None, splitter=None)

Bases: spinn_front_end_common.abstract_models.impl.tdma_aware_application_vertex.TDMAAwareApplicationVertex, 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.common.simple_population_settable.SimplePopulationSettable, spinn_front_end_common.abstract_models.impl.provides_key_to_atom_mapping_impl.ProvidesKeyToAtomMappingImpl, pacman.model.partitioner_interfaces.legacy_partitioner_api.LegacyPartitionerAPI

A Poisson Spike source object

Parameters:

• n_neurons (int) –
• constraints (iterable(AbstractConstraint)) –
• label (str) –
• seed (float) –
• max_atoms_per_core (int) –
• model (SpikeSourcePoisson) –
• rate (iterable(float)) –
• start (iterable(int)) –
• duration (iterable(int)) –
• splitter (AbstractSplitterCommon) –

SPIKE_RECORDING_REGION_ID = 0

clear_spike_recording(buffer_manager, placements)

Clear the recorded data from the object

Parameters:

• buffer_manager (BufferManager) – the buffer manager object
• placements (Placements) – the placements object

Return type:

None

create_machine_vertex(vertex_slice, resources_required, label=None, constraints=None)

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.

Returns:

The created machine vertex

Return type:

MachineVertex

describe()

Return 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.

Return type:dict(str, ..)

duration

durations

static get_cpu_usage_for_atoms()

static get_dtcm_usage_for_atoms()

get_outgoing_partition_constraints(partition)

Get constraints to be added to the given edge partition 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_recording_sdram_usage(vertex_slice)

Parameters:vertex_slice (Slice) –

get_resources_used_by_atoms(vertex_slice)

Get the separate resource requirements for a range of atoms.

Parameters:

• vertex_slice (Slice) – the low value of atoms to calculate resources from
• vertex_slice –

Returns:

a resource container that contains a CPUCyclesPerTickResource, DTCMResource and SDRAMResource

Return type:

ResourceContainer

get_spikes(placements, buffer_manager)

Get the recorded spikes from the object

Parameters:

• placements (Placements) – the placements object
• buffer_manager (BufferManager) – the buffer manager object

Returns:

A numpy array of 2-element arrays of (neuron_id, time) ordered by time, one element per event

Return type:

ndarray(tuple(int,int))

get_spikes_sampling_interval()

Return the current sampling interval for spikes

Returns:Sampling interval in microseconds Return type:float

is_recording_spikes()

Determine if spikes are being recorded

Returns:True if spikes are being recorded, False otherwise Return type:bool

kiss_seed(vertex_slice)

mark_no_changes()

Marks the point after which changes are reported, so that new changes can be detected before the next check.

max_rate

max_spikes_per_ts()

n_atoms

The number of atoms in the vertex

Return type:int

n_profile_samples

rate

rate_change

rates

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

seed

set_recording_spikes(new_state=True, sampling_interval=None, indexes=None)

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 (int or None) – The interval at which spikes are recorded. Must be a whole multiple of the timestep. None will be taken as the timestep.
• indexes (list(int) or None) – The indexes of the neurons that will record spikes. If None the assumption is all neurons are recording

set_value(key, value)

Set a property

Parameters:

• key (str) – the name of the parameter to change
• value (Any or float or int or list(float) or list(int)) – the new value of the parameter to assign

start

starts

time_to_spike

update_kiss_seed(vertex_slice, seed)

updates a kiss seed from the machine

Parameters:

• vertex_slice – the vertex slice to update seed of
• seed – the seed

Return type:

None

Module contents

class spynnaker.pyNN.models.spike_source.SpikeSourceArray(spike_times=None)

Bases: spynnaker.pyNN.models.abstract_pynn_model.AbstractPyNNModel

create_vertex(n_neurons, label, constraints, splitter)

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(AbstractConstraint) or None) – A list of constraints to give to the vertex, or None

Returns:

An application vertex for the population

Return type:

ApplicationVertex

default_population_parameters = {'splitter': None}

class spynnaker.pyNN.models.spike_source.SpikeSourceArrayVertex(n_neurons, spike_times, constraints, label, max_atoms_per_core, model, splitter)

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)

Clear the recorded data from the object

Parameters:

• buffer_manager (BufferManager) – the buffer manager object
• placements (Placements) – the placements object

Return type:

None

describe()

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, buffer_manager)

Get the recorded spikes from the object

Parameters:

• placements (Placements) – the placements object
• buffer_manager (BufferManager) – the buffer manager object

Returns:

A numpy array of 2-element arrays of (neuron_id, time) ordered by time, one element per event

Return type:

ndarray(tuple(int,int))

get_spikes_sampling_interval()

Return the current sampling interval for spikes

Returns:Sampling interval in microseconds Return type:float

is_recording_spikes()

Determine if spikes are being recorded

Returns:True if spikes are being recorded, False otherwise Return type:bool

mark_no_changes()

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)

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 (int or None) – The interval at which spikes are recorded. Must be a whole multiple of the timestep. None will be taken as the timestep.
• indexes (list(int) or None) – The indexes of the neurons that will record spikes. If None the assumption is all neurons are recording

set_value_by_selector(selector, key, value)

Sets the value for a particular key but only for the selected subset.

Parameters:

• selector (None or slice or int or list(bool) or list(int)) – See RangedList.set_value_by_selector as this is just a pass through method
• key (str) – the name of the parameter to change
• value (float or int or list(float) or list(int)) – the new value of the parameter to assign

spike_times

The spike times of the spike source array

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')

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)

Bases: spynnaker.pyNN.models.abstract_pynn_model.AbstractPyNNModel

create_vertex(n_neurons, label, constraints, seed, max_rate, splitter)

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(AbstractConstraint) or None) – A list of constraints to give to the vertex, or None

Returns:

An application vertex for the population

Return type:

ApplicationVertex

default_population_parameters = {'max_rate': None, 'seed': None, 'splitter': None}

classmethod get_max_atoms_per_core()

Get the maximum number of atoms per core for this model

Return type:int

classmethod set_model_max_atoms_per_core(n_atoms=500)

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

class spynnaker.pyNN.models.spike_source.SpikeSourcePoissonMachineVertex(resources_required, is_recording, constraints=None, label=None, app_vertex=None, vertex_slice=None)

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_supports_database_injection.AbstractSupportsDatabaseInjection, spinn_front_end_common.interface.profiling.abstract_has_profile_data.AbstractHasProfileData, spinn_front_end_common.abstract_models.abstract_has_associated_binary.AbstractHasAssociatedBinary, spinn_front_end_common.abstract_models.abstract_rewrites_data_specification.AbstractRewritesDataSpecification, spinn_front_end_common.abstract_models.abstract_generates_data_specification.AbstractGeneratesDataSpecification, spynnaker.pyNN.models.abstract_models.abstract_read_parameters_before_set.AbstractReadParametersBeforeSet

class EXTRA_PROVENANCE_DATA_ENTRIES

Bases: enum.Enum

Entries for the provenance data generated by standard neuron models.

TDMA_MISSED_SLOTS = 0

The number of pre-synaptic events

FAST_RATE_PER_TICK_CUTOFF = 10

PARAMS_BASE_WORDS = 13

class POISSON_SPIKE_SOURCE_REGIONS

Bases: enum.Enum

An enumeration.

POISSON_PARAMS_REGION = 1

PROFILER_REGION = 5

PROVENANCE_REGION = 4

RATES_REGION = 2

SPIKE_HISTORY_REGION = 3

SYSTEM_REGION = 0

TDMA_REGION = 6

PROFILE_TAG_LABELS = {0: 'TIMER', 1: 'PROB_FUNC'}

SEED_OFFSET_BYTES = 36

SEED_SIZE_BYTES = 16

SLOW_RATE_PER_TICK_CUTOFF = 0.01

generate_data_specification(spec, placement, routing_info, data_n_time_steps, graph, first_machine_time_step)

Generate a data specification.

Parameters:

• spec (DataSpecificationGenerator) – The data specification to write to
• placement (Placement) – The placement the vertex is located at
• routing_info (RoutingInfo) –
• data_n_time_steps (int) –
• graph (MachineGraph) –
• first_machine_time_step (int) –

Return type:

None

get_binary_file_name()

Get the binary name to be run for this vertex.

Return type:str

get_binary_start_type()

Get the start type of the binary to be run.

Return type:ExecutableType

get_profile_data(transceiver, placement)

Get the profile data recorded during simulation

Parameters:

• transceiver (Transceiver) –
• placement (Placement) –

Return type:

ProfileData

get_recorded_region_ids()

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)

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

Whether this vertex is actually in injection mode.

Return type:bool

max_spikes_per_second()

Get maximum expected number of spikes per second

Parameters:variable (str) – the variable to find units from Returns:the units as a string. Return type:str

max_spikes_per_ts()

Get maximum expected number of spikes per timestep

Return type:int

parse_extra_provenance_items(label, names, provenance_data)

Convert the remaining provenance words (those not in the standard set) into provenance items.

Called by get_provenance_data_from_machine()

Parameters:

• label (str) – A descriptive label for the vertex (derived from label and placed position) to be used for provenance error reporting to the user.
• names (list(str)) – The base names describing the location of the machine vertex producing the provenance.
• provenance_data (list(int)) – The list of words of raw provenance data.

Returns:

The interpreted provenance items.

Return type:

iterable(ProvenanceDataItem)

poisson_param_region_address(placement, transceiver)

poisson_rate_region_address(placement, transceiver)

read_parameters_from_machine(transceiver, placement, vertex_slice)

Read the parameters from the machine before any are changed.

Parameters:

• transceiver (Transceiver) – the SpinnMan interface
• placement (Placement) – the placement of a vertex
• vertex_slice (Slice) – the slice of atoms for this vertex

Return type:

None

regenerate_data_specification(spec, placement, routing_info, graph, first_machine_time_step)

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?
• routing_info (RoutingInfo) –
• graph (MachineGraph) –
• first_machine_time_step (int) –

reload_required(first_machine_time_step)

Return true if any data region needs to be reloaded

Return type:bool

reserve_memory_regions(spec, placement)

Reserve memory regions for Poisson source parameters and output buffer.

Parameters:

• spec (DataSpecificationGenerator) – the data specification writer
• placement (Placement) – the location this vertex resides on in the machine

Returns:

None

resources_required

The resources required by the vertex

Return type:ResourceContainer

set_reload_required(new_value)

Indicate that the regions have been reloaded

Parameters:new_value – the new value Return type:None

class spynnaker.pyNN.models.spike_source.SpikeSourcePoissonVariable(rates, starts, durations=None)

Bases: spynnaker.pyNN.models.abstract_pynn_model.AbstractPyNNModel

create_vertex(n_neurons, label, constraints, seed, splitter)

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(AbstractConstraint) or None) – A list of constraints to give to the vertex, or None

Returns:

An application vertex for the population

Return type:

ApplicationVertex

default_population_parameters = {'seed': None, 'splitter': None}

classmethod get_max_atoms_per_core()

Get the maximum number of atoms per core for this model

Return type:int

classmethod set_model_max_atoms_per_core(n_atoms=500)

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