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

Module contents

class spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.AbstractHasAPlusAMinus

Bases: object

An object that has \(A^+\) and \(A^-\) properties.

property A_minus

Settable model parameter: \(A^-\)

Return type:

float

property A_plus

Settable model parameter: \(A^+\)

Return type:

float

set_a_plus_a_minus(a_plus, a_minus)

Set the values of \(A^+\) and \(A^-\).

Parameters:

• a_plus (float) – \(A^+\)

• a_minus (float) – \(A^-\)

class spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.AbstractWeightDependence

Bases: object

abstract get_parameter_names()

Returns the parameter names.

Return type:

iterable(str)

abstract get_parameters_sdram_usage_in_bytes(n_synapse_types, n_weight_terms)

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

Parameters:

• n_synapse_types (int) –

• n_weight_terms (int) –

Return type:

int

abstract is_same_as(weight_dependence)

Determine if this weight dependence is the same as another.

Parameters:

weight_dependence (AbstractWeightDependence) –

Return type:

bool

abstract property vertex_executable_suffix

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

Return type:

str

abstract property weight_maximum

The maximum weight that will ever be set in a synapse as a result of this rule.

Return type:

float

abstract write_parameters(spec, global_weight_scale, synapse_weight_scales, n_weight_terms)

Write the parameters of the rule to the spec.

Parameters:

• spec (DataSpecificationGenerator) – The specification to write to

• global_weight_scale (float) – The weight scale applied globally

• synapse_weight_scales (list(float)) – The total weight scale applied to each synapse including the global weight scale

• n_weight_terms (int) – The number of terms used by the synapse rule

class spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.WeightDependenceAdditive(w_min=0.0, w_max=1.0)

Bases: AbstractHasAPlusAMinus, AbstractWeightDependence

An additive weight dependence STDP rule.

Parameters:

• w_min (float) – \(w^{min}\)

• w_max (float) – \(w^{max}\)

get_parameter_names()

Returns the parameter names.

Return type:

iterable(str)

get_parameters_sdram_usage_in_bytes(n_synapse_types, n_weight_terms)

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

Parameters:

• n_synapse_types (int) –

• n_weight_terms (int) –

Return type:

int

is_same_as(weight_dependence)

Determine if this weight dependence is the same as another.

Parameters:

weight_dependence (AbstractWeightDependence) –

Return type:

bool

property vertex_executable_suffix

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

Return type:

str

property w_max

\(w^{max}\)

Return type:

float

property w_min

\(w^{min}\)

Return type:

float

property weight_maximum

The maximum weight that will ever be set in a synapse as a result of this rule.

Return type:

float

write_parameters(spec, global_weight_scale, synapse_weight_scales, n_weight_terms)

Write the parameters of the rule to the spec.

Parameters:

• spec (DataSpecificationGenerator) – The specification to write to

• global_weight_scale (float) – The weight scale applied globally

• synapse_weight_scales (list(float)) – The total weight scale applied to each synapse including the global weight scale

• n_weight_terms (int) – The number of terms used by the synapse rule

class spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.WeightDependenceAdditiveTriplet(w_min=0.0, w_max=1.0, A3_plus=0.01, A3_minus=0.01)

Bases: AbstractHasAPlusAMinus, AbstractWeightDependence

An triplet-based additive weight dependence STDP rule.

Parameters:

• w_min (float) – \(w^{min}\)

• w_max (float) – \(w^{max}\)

• A3_plus (float) – \(A_3^+\)

• A3_minus (float) – \(A_3^-\)

property A3_minus

\(A_3^-\)

Return type:

float

property A3_plus

\(A_3^+\)

Return type:

float

default_parameters = {'A3_minus': 0.01, 'A3_plus': 0.01, 'w_max': 1.0, 'w_min': 0.0}

get_parameter_names()

Returns the parameter names.

Return type:

iterable(str)

get_parameters_sdram_usage_in_bytes(n_synapse_types, n_weight_terms)

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

Parameters:

• n_synapse_types (int) –

• n_weight_terms (int) –

Return type:

int

is_same_as(weight_dependence)

Determine if this weight dependence is the same as another.

Parameters:

weight_dependence (AbstractWeightDependence) –

Return type:

bool

property vertex_executable_suffix

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

Return type:

str

property w_max

\(w^{max}\)

Return type:

float

property w_min

\(w^{min}\)

Return type:

float

property weight_maximum

The maximum weight that will ever be set in a synapse as a result of this rule.

Return type:

float

write_parameters(spec, global_weight_scale, synapse_weight_scales, n_weight_terms)

Write the parameters of the rule to the spec.

Parameters:

• spec (DataSpecificationGenerator) – The specification to write to

• global_weight_scale (float) – The weight scale applied globally

• synapse_weight_scales (list(float)) – The total weight scale applied to each synapse including the global weight scale

• n_weight_terms (int) – The number of terms used by the synapse rule

class spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.WeightDependenceMultiplicative(w_min=0.0, w_max=1.0)

Bases: AbstractHasAPlusAMinus, AbstractWeightDependence

A multiplicative weight dependence STDP rule.

Parameters:

• w_min (float) – \(w^{min}\)

• w_max (float) – \(w^{max}\)

get_parameter_names()

Returns the parameter names.

Return type:

iterable(str)

get_parameters_sdram_usage_in_bytes(n_synapse_types, n_weight_terms)

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

Parameters:

• n_synapse_types (int) –

• n_weight_terms (int) –

Return type:

int

is_same_as(weight_dependence)

Determine if this weight dependence is the same as another.

Parameters:

weight_dependence (AbstractWeightDependence) –

Return type:

bool

property vertex_executable_suffix

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

Return type:

str

property w_max

\(w^{max}\)

Return type:

float

property w_min

\(w^{min}\)

Return type:

float

property weight_maximum

The maximum weight that will ever be set in a synapse as a result of this rule.

Return type:

float

write_parameters(spec, global_weight_scale, synapse_weight_scales, n_weight_terms)

Write the parameters of the rule to the spec.

Parameters:

• spec (DataSpecificationGenerator) – The specification to write to

• global_weight_scale (float) – The weight scale applied globally

• synapse_weight_scales (list(float)) – The total weight scale applied to each synapse including the global weight scale

• n_weight_terms (int) – The number of terms used by the synapse rule