spynnaker.pyNN.models.neuron.synapse_dynamics package

Submodules

spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_generate_on_machine module

class spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_generate_on_machine.AbstractGenerateOnMachine[source]

Bases: object

A 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
generate_on_machine()[source]

Determines if this instance should be generated on the machine.

Default implementation returns True

Return type:bool
class spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_generate_on_machine.MatrixGeneratorID[source]

Bases: enum.Enum

An enumeration.

STATIC_MATRIX = 0
STDP_MATRIX = 1

spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_plastic_synapse_dynamics module

class spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_plastic_synapse_dynamics.AbstractPlasticSynapseDynamics[source]

Bases: spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_synapse_dynamics.AbstractSynapseDynamics

Synapses 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.

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

spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_static_synapse_dynamics module

class spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_static_synapse_dynamics.AbstractStaticSynapseDynamics[source]

Bases: spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_synapse_dynamics.AbstractSynapseDynamics

Dynamics 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_synapses_in_rows(ff_size)[source]

Get the number of synapses in the rows with sizes ff_size

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.

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

spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_synapse_dynamics module

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')]
are_weights_signed()[source]

Determines if the weights are signed values

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_delay_maximum(connector, delays)[source]

Get the maximum delay for the synapses

get_delay_variance(connector, delays)[source]

Get the variance in delay for the synapses

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

get_provenance_data(pre_population_label, post_population_label)[source]

Get the provenance data from this synapse dynamics object

get_vertex_executable_suffix()[source]

Get the executable suffix for a vertex for this dynamics

get_weight_maximum(connector, weights)[source]

Get the maximum weight for the synapses

get_weight_mean(connector, weights)[source]

Get the mean weight for the synapses

get_weight_variance(connector, weights)[source]

Get the variance in weight for the synapses

get_words(rows)[source]

Convert the row data to words

is_same_as(synapse_dynamics)[source]

Determines if this synapse dynamics is the same as another

write_parameters(spec, region, machine_time_step, weight_scales)[source]

Write the synapse parameters to the spec

spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_synapse_dynamics_structural module

class spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_synapse_dynamics_structural.AbstractSynapseDynamicsStructural[source]

Bases: object

spynnaker.pyNN.models.neuron.synapse_dynamics.pynn_synapse_dynamics module

class spynnaker.pyNN.models.neuron.synapse_dynamics.pynn_synapse_dynamics.PyNNSynapseDynamics(slow=None, fast=None)[source]

Bases: object

slow

spynnaker.pyNN.models.neuron.synapse_dynamics.structural_dynamics module

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

spynnaker.pyNN.models.neuron.synapse_dynamics.synapse_dynamics_static module

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

are_weights_signed()[source]

Determines if the weights are signed values

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_synapses_in_rows(ff_size)[source]

Get the number of synapses in the rows with sizes ff_size

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.

get_value(key)[source]

Get a property Get a property

get_vertex_executable_suffix()[source]

Get the executable suffix for a vertex for this dynamics

is_same_as(synapse_dynamics)[source]

Determines if this synapse dynamics is the same as another

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!
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
write_parameters(spec, region, machine_time_step, weight_scales)[source]

Write the synapse parameters to the spec

spynnaker.pyNN.models.neuron.synapse_dynamics.synapse_dynamics_stdp module

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

are_weights_signed()[source]

Determines if the weights are signed values

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

get_value(key)[source]

Get a property Get a property

get_vertex_executable_suffix()[source]

Get the executable suffix for a vertex for this dynamics

get_weight_maximum(connector, weights)[source]

Get the maximum weight for the synapses

get_weight_mean(connector, weights)[source]

Get the mean weight for the synapses

get_weight_variance(connector, weights)[source]

Get the variance in weight for the synapses

is_same_as(synapse_dynamics)[source]

Determines if this synapse dynamics is the same as another

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
write_parameters(spec, region, machine_time_step, weight_scales)[source]

Write the synapse parameters to the spec

spynnaker.pyNN.models.neuron.synapse_dynamics.synapse_dynamics_structural_common module

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]

Bases: spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_synapse_dynamics_structural.AbstractSynapseDynamicsStructural

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_n_synapses_in_rows(pp_size, fp_size=None)[source]

Get number of synapses in a row.

get_parameter_names()[source]
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
get_vertex_executable_suffix()[source]
is_same_as(synapse_dynamics)[source]
n_words_for_plastic_connections(value)[source]

Get size of plastic connections in words

n_words_for_static_connections(value)[source]

Get size of static connections in words

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

spynnaker.pyNN.models.neuron.synapse_dynamics.synapse_dynamics_structural_static module

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.

get_vertex_executable_suffix()[source]

Get the executable suffix for a vertex for this dynamics

is_same_as(synapse_dynamics)[source]

Determines if this synapse dynamics is the same as another

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

spynnaker.pyNN.models.neuron.synapse_dynamics.synapse_dynamics_structural_stdp module

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.

get_vertex_executable_suffix()[source]

Get the executable suffix for a vertex for this dynamics

is_same_as(synapse_dynamics)[source]

Determines if this synapse dynamics is the same as another

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

Module contents

class spynnaker.pyNN.models.neuron.synapse_dynamics.AbstractSynapseDynamics[source]

Bases: object

NUMPY_CONNECTORS_DTYPE = [('source', 'uint32'), ('target', 'uint32'), ('weight', 'float64'), ('delay', 'float64')]
are_weights_signed()[source]

Determines if the weights are signed values

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_delay_maximum(connector, delays)[source]

Get the maximum delay for the synapses

get_delay_variance(connector, delays)[source]

Get the variance in delay for the synapses

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

get_provenance_data(pre_population_label, post_population_label)[source]

Get the provenance data from this synapse dynamics object

get_vertex_executable_suffix()[source]

Get the executable suffix for a vertex for this dynamics

get_weight_maximum(connector, weights)[source]

Get the maximum weight for the synapses

get_weight_mean(connector, weights)[source]

Get the mean weight for the synapses

get_weight_variance(connector, weights)[source]

Get the variance in weight for the synapses

get_words(rows)[source]

Convert the row data to words

is_same_as(synapse_dynamics)[source]

Determines if this synapse dynamics is the same as another

write_parameters(spec, region, machine_time_step, weight_scales)[source]

Write the synapse parameters to the spec

class spynnaker.pyNN.models.neuron.synapse_dynamics.AbstractGenerateOnMachine[source]

Bases: object

A 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
generate_on_machine()[source]

Determines if this instance should be generated on the machine.

Default implementation returns True

Return type:bool
class spynnaker.pyNN.models.neuron.synapse_dynamics.AbstractStaticSynapseDynamics[source]

Bases: spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_synapse_dynamics.AbstractSynapseDynamics

Dynamics 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_synapses_in_rows(ff_size)[source]

Get the number of synapses in the rows with sizes ff_size

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.

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

class spynnaker.pyNN.models.neuron.synapse_dynamics.AbstractPlasticSynapseDynamics[source]

Bases: spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_synapse_dynamics.AbstractSynapseDynamics

Synapses 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.

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

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

are_weights_signed()[source]

Determines if the weights are signed values

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_synapses_in_rows(ff_size)[source]

Get the number of synapses in the rows with sizes ff_size

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.

get_value(key)[source]

Get a property Get a property

get_vertex_executable_suffix()[source]

Get the executable suffix for a vertex for this dynamics

is_same_as(synapse_dynamics)[source]

Determines if this synapse dynamics is the same as another

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!
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
write_parameters(spec, region, machine_time_step, weight_scales)[source]

Write the synapse parameters to the spec

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

are_weights_signed()[source]

Determines if the weights are signed values

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

get_value(key)[source]

Get a property Get a property

get_vertex_executable_suffix()[source]

Get the executable suffix for a vertex for this dynamics

get_weight_maximum(connector, weights)[source]

Get the maximum weight for the synapses

get_weight_mean(connector, weights)[source]

Get the mean weight for the synapses

get_weight_variance(connector, weights)[source]

Get the variance in weight for the synapses

is_same_as(synapse_dynamics)[source]

Determines if this synapse dynamics is the same as another

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
write_parameters(spec, region, machine_time_step, weight_scales)[source]

Write the synapse parameters to the spec

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]

Bases: spynnaker.pyNN.models.neuron.synapse_dynamics.abstract_synapse_dynamics_structural.AbstractSynapseDynamicsStructural

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_n_synapses_in_rows(pp_size, fp_size=None)[source]

Get number of synapses in a row.

get_parameter_names()[source]
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
get_vertex_executable_suffix()[source]
is_same_as(synapse_dynamics)[source]
n_words_for_plastic_connections(value)[source]

Get size of plastic connections in words

n_words_for_static_connections(value)[source]

Get size of static connections in words

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.

get_vertex_executable_suffix()[source]

Get the executable suffix for a vertex for this dynamics

is_same_as(synapse_dynamics)[source]

Determines if this synapse dynamics is the same as another

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

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.

get_vertex_executable_suffix()[source]

Get the executable suffix for a vertex for this dynamics

is_same_as(synapse_dynamics)[source]

Determines if this synapse dynamics is the same as another

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