spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence package

Submodules

spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.abstract_timing_dependence module

class spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.abstract_timing_dependence.AbstractTimingDependence

Bases: object

get_parameter_names()

Return the names of the parameters supported by this timing dependency model.

Return type:iterable(str)

get_parameters_sdram_usage_in_bytes()

Get the amount of SDRAM used by the parameters of this rule

get_provenance_data(pre_population_label, post_population_label)

Get any provenance data

is_same_as(timing_dependence)

Determine if this timing dependence is the same as another

n_weight_terms

The number of weight terms expected by this timing rule

pre_trace_n_bytes

The number of bytes used by the pre-trace of the rule per neuron

synaptic_structure

Get the synaptic structure of the plastic part of the rows

vertex_executable_suffix

The suffix to be appended to the vertex executable for this rule

write_parameters(spec, machine_time_step, weight_scales)

Write the parameters of the rule to the spec

spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_pfister_spike_triplet module

class spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_pfister_spike_triplet.TimingDependencePfisterSpikeTriplet(tau_plus, tau_minus, tau_x, tau_y)

Bases: spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.abstract_timing_dependence.AbstractTimingDependence

get_parameter_names()

Return the names of the parameters supported by this timing dependency model.

Return type:iterable(str)

get_parameters_sdram_usage_in_bytes()

Get the amount of SDRAM used by the parameters of this rule

get_provenance_data(pre_population_label, post_population_label)

Get any provenance data

is_same_as(timing_dependence)

Determine if this timing dependence is the same as another

n_weight_terms

The number of weight terms expected by this timing rule

pre_trace_n_bytes

The number of bytes used by the pre-trace of the rule per neuron

synaptic_structure

Get the synaptic structure of the plastic part of the rows

tau_minus

tau_plus

tau_x

tau_y

vertex_executable_suffix

The suffix to be appended to the vertex executable for this rule

write_parameters(spec, machine_time_step, weight_scales)

Write the parameters of the rule to the spec

spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_recurrent module

class spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_recurrent.TimingDependenceRecurrent(accumulator_depression=-6, accumulator_potentiation=6, mean_pre_window=35.0, mean_post_window=35.0, dual_fsm=True)

Bases: spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.abstract_timing_dependence.AbstractTimingDependence

default_parameters = {'accumulator_depression': -6, 'accumulator_potentiation': 6, 'dual_fsm': True, 'mean_post_window': 35.0, 'mean_pre_window': 35.0}

get_parameter_names()

Return the names of the parameters supported by this timing dependency model.

Return type:iterable(str)

get_parameters_sdram_usage_in_bytes()

Get the amount of SDRAM used by the parameters of this rule

is_same_as(timing_dependence)

Determine if this timing dependence is the same as another

n_weight_terms

The number of weight terms expected by this timing rule

pre_trace_n_bytes

The number of bytes used by the pre-trace of the rule per neuron

synaptic_structure

Get the synaptic structure of the plastic part of the rows

vertex_executable_suffix

The suffix to be appended to the vertex executable for this rule

write_parameters(spec, machine_time_step, weight_scales)

Write the parameters of the rule to the spec

spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_spike_nearest_pair module

class spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_spike_nearest_pair.TimingDependenceSpikeNearestPair(tau_plus=20.0, tau_minus=20.0)

Bases: spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.abstract_timing_dependence.AbstractTimingDependence

default_parameters = {'tau_minus': 20.0, 'tau_plus': 20.0}

get_parameter_names()

Return the names of the parameters supported by this timing dependency model.

Return type:iterable(str)

get_parameters_sdram_usage_in_bytes()

Get the amount of SDRAM used by the parameters of this rule

get_provenance_data(pre_population_label, post_population_label)

Get any provenance data

is_same_as(timing_dependence)

Determine if this timing dependence is the same as another

n_weight_terms

The number of weight terms expected by this timing rule

pre_trace_n_bytes

The number of bytes used by the pre-trace of the rule per neuron

synaptic_structure

Get the synaptic structure of the plastic part of the rows

tau_minus

tau_plus

vertex_executable_suffix

The suffix to be appended to the vertex executable for this rule

write_parameters(spec, machine_time_step, weight_scales)

Write the parameters of the rule to the spec

spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_spike_pair module

class spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_spike_pair.TimingDependenceSpikePair(tau_plus=20.0, tau_minus=20.0)

Bases: spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.abstract_timing_dependence.AbstractTimingDependence

get_parameter_names()

Return the names of the parameters supported by this timing dependency model.

Return type:iterable(str)

get_parameters_sdram_usage_in_bytes()

Get the amount of SDRAM used by the parameters of this rule

get_provenance_data(pre_population_label, post_population_label)

Get any provenance data

is_same_as(timing_dependence)

Determine if this timing dependence is the same as another

n_weight_terms

The number of weight terms expected by this timing rule

pre_trace_n_bytes

The number of bytes used by the pre-trace of the rule per neuron

synaptic_structure

Get the synaptic structure of the plastic part of the rows

tau_minus

tau_plus

vertex_executable_suffix

The suffix to be appended to the vertex executable for this rule

write_parameters(spec, machine_time_step, weight_scales)

Write the parameters of the rule to the spec

spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_vogels_2011 module

class spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_vogels_2011.TimingDependenceVogels2011(alpha, tau=20.0)

Bases: spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.abstract_timing_dependence.AbstractTimingDependence

alpha

default_parameters = {'tau': 20.0}

get_parameter_names()

Return the names of the parameters supported by this timing dependency model.

Return type:iterable(str)

get_parameters_sdram_usage_in_bytes()

Get the amount of SDRAM used by the parameters of this rule

is_same_as(timing_dependence)

Determine if this timing dependence is the same as another

n_weight_terms

The number of weight terms expected by this timing rule

pre_trace_n_bytes

The number of bytes used by the pre-trace of the rule per neuron

synaptic_structure

Get the synaptic structure of the plastic part of the rows

tau

vertex_executable_suffix

The suffix to be appended to the vertex executable for this rule

write_parameters(spec, machine_time_step, weight_scales)

Write the parameters of the rule to the spec

Module contents

class spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.AbstractTimingDependence

Bases: object

get_parameter_names()

Return the names of the parameters supported by this timing dependency model.

Return type:iterable(str)

get_parameters_sdram_usage_in_bytes()

Get the amount of SDRAM used by the parameters of this rule

get_provenance_data(pre_population_label, post_population_label)

Get any provenance data

is_same_as(timing_dependence)

Determine if this timing dependence is the same as another

n_weight_terms

The number of weight terms expected by this timing rule

pre_trace_n_bytes

The number of bytes used by the pre-trace of the rule per neuron

synaptic_structure

Get the synaptic structure of the plastic part of the rows

vertex_executable_suffix

The suffix to be appended to the vertex executable for this rule

write_parameters(spec, machine_time_step, weight_scales)

Write the parameters of the rule to the spec

class spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.TimingDependenceSpikePair(tau_plus=20.0, tau_minus=20.0)

Bases: spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.abstract_timing_dependence.AbstractTimingDependence

get_parameter_names()

Return the names of the parameters supported by this timing dependency model.

Return type:iterable(str)

get_parameters_sdram_usage_in_bytes()

Get the amount of SDRAM used by the parameters of this rule

get_provenance_data(pre_population_label, post_population_label)

Get any provenance data

is_same_as(timing_dependence)

Determine if this timing dependence is the same as another

n_weight_terms

The number of weight terms expected by this timing rule

pre_trace_n_bytes

The number of bytes used by the pre-trace of the rule per neuron

synaptic_structure

Get the synaptic structure of the plastic part of the rows

tau_minus

tau_plus

vertex_executable_suffix

The suffix to be appended to the vertex executable for this rule

write_parameters(spec, machine_time_step, weight_scales)

Write the parameters of the rule to the spec

class spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.TimingDependencePfisterSpikeTriplet(tau_plus, tau_minus, tau_x, tau_y)

Bases: spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.abstract_timing_dependence.AbstractTimingDependence

get_parameter_names()

Return the names of the parameters supported by this timing dependency model.

Return type:iterable(str)

get_parameters_sdram_usage_in_bytes()

Get the amount of SDRAM used by the parameters of this rule

get_provenance_data(pre_population_label, post_population_label)

Get any provenance data

is_same_as(timing_dependence)

Determine if this timing dependence is the same as another

n_weight_terms

The number of weight terms expected by this timing rule

pre_trace_n_bytes

The number of bytes used by the pre-trace of the rule per neuron

synaptic_structure

Get the synaptic structure of the plastic part of the rows

tau_minus

tau_plus

tau_x

tau_y

vertex_executable_suffix

The suffix to be appended to the vertex executable for this rule

write_parameters(spec, machine_time_step, weight_scales)

Write the parameters of the rule to the spec

class spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.TimingDependenceRecurrent(accumulator_depression=-6, accumulator_potentiation=6, mean_pre_window=35.0, mean_post_window=35.0, dual_fsm=True)

Bases: spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.abstract_timing_dependence.AbstractTimingDependence

default_parameters = {'accumulator_depression': -6, 'accumulator_potentiation': 6, 'dual_fsm': True, 'mean_post_window': 35.0, 'mean_pre_window': 35.0}

get_parameter_names()

Return the names of the parameters supported by this timing dependency model.

Return type:iterable(str)

get_parameters_sdram_usage_in_bytes()

Get the amount of SDRAM used by the parameters of this rule

is_same_as(timing_dependence)

Determine if this timing dependence is the same as another

n_weight_terms

The number of weight terms expected by this timing rule

pre_trace_n_bytes

The number of bytes used by the pre-trace of the rule per neuron

synaptic_structure

Get the synaptic structure of the plastic part of the rows

vertex_executable_suffix

The suffix to be appended to the vertex executable for this rule

write_parameters(spec, machine_time_step, weight_scales)

Write the parameters of the rule to the spec

class spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.TimingDependenceSpikeNearestPair(tau_plus=20.0, tau_minus=20.0)

Bases: spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.abstract_timing_dependence.AbstractTimingDependence

default_parameters = {'tau_minus': 20.0, 'tau_plus': 20.0}

get_parameter_names()

Return the names of the parameters supported by this timing dependency model.

Return type:iterable(str)

get_parameters_sdram_usage_in_bytes()

Get the amount of SDRAM used by the parameters of this rule

get_provenance_data(pre_population_label, post_population_label)

Get any provenance data

is_same_as(timing_dependence)

Determine if this timing dependence is the same as another

n_weight_terms

The number of weight terms expected by this timing rule

pre_trace_n_bytes

The number of bytes used by the pre-trace of the rule per neuron

synaptic_structure

Get the synaptic structure of the plastic part of the rows

tau_minus

tau_plus

vertex_executable_suffix

The suffix to be appended to the vertex executable for this rule

write_parameters(spec, machine_time_step, weight_scales)

Write the parameters of the rule to the spec

class spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.TimingDependenceVogels2011(alpha, tau=20.0)

Bases: spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.abstract_timing_dependence.AbstractTimingDependence

alpha

default_parameters = {'tau': 20.0}

get_parameter_names()

Return the names of the parameters supported by this timing dependency model.

Return type:iterable(str)

get_parameters_sdram_usage_in_bytes()

Get the amount of SDRAM used by the parameters of this rule

is_same_as(timing_dependence)

Determine if this timing dependence is the same as another

n_weight_terms

The number of weight terms expected by this timing rule

pre_trace_n_bytes

The number of bytes used by the pre-trace of the rule per neuron

synaptic_structure

Get the synaptic structure of the plastic part of the rows

tau

vertex_executable_suffix

The suffix to be appended to the vertex executable for this rule

write_parameters(spec, machine_time_step, weight_scales)

Write the parameters of the rule to the spec