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

Module contents

class spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.AbstractHasAPlusAMinus[source]

Bases: object

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

A_minus

Settable model parameter: \(A^-\)

Return type:float
A_plus

Settable model parameter: \(A^+\)

Return type:float
set_a_plus_a_minus(a_plus, a_minus)[source]

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[source]

Bases: object

get_parameter_names()[source]

Returns the parameter names

Return type:iterable(str)
get_parameters_sdram_usage_in_bytes(n_synapse_types, n_weight_terms)[source]

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

Parameters:
  • n_synapse_types (int) –
  • n_weight_terms (int) –
Return type:

int

get_provenance_data(pre_population_label, post_population_label)[source]

Get any provenance data

Parameters:
  • pre_population_label (str) – label of pre.
  • post_population_label (str) – label of post.
Returns:

the provenance data of the weight dependency

Return type:

iterable(ProvenanceDataItem)

is_same_as(weight_dependence)[source]

Determine if this weight dependence is the same as another

Parameters:weight_dependence (AbstractWeightDependence) –
Return type:bool
vertex_executable_suffix

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

Return type:str
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, weight_scales, n_weight_terms)[source]

Write the parameters of the rule to the spec

Parameters:
class spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.WeightDependenceAdditive(w_min=0.0, w_max=1.0)[source]

Bases: spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.abstract_has_a_plus_a_minus.AbstractHasAPlusAMinus, spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.abstract_weight_dependence.AbstractWeightDependence

An additive weight dependence STDP rule.

Parameters:
  • w_min (float) – \(w^{min}\)
  • w_max (float) – \(w^{max}\)
get_parameter_names()[source]

Returns the parameter names

Return type:iterable(str)
get_parameters_sdram_usage_in_bytes(n_synapse_types, n_weight_terms)[source]

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)[source]

Determine if this weight dependence is the same as another

Parameters:weight_dependence (AbstractWeightDependence) –
Return type:bool
vertex_executable_suffix

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

Return type:str
w_max

\(w^{max}\)

Return type:float
w_min

\(w^{min}\)

Return type:float
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, weight_scales, n_weight_terms)[source]

Write the parameters of the rule to the spec

Parameters:
class spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.WeightDependenceMultiplicative(w_min=0.0, w_max=1.0)[source]

Bases: spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.abstract_has_a_plus_a_minus.AbstractHasAPlusAMinus, spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.abstract_weight_dependence.AbstractWeightDependence

A multiplicative weight dependence STDP rule.

Parameters:
  • w_min (float) – \(w^{min}\)
  • w_max (float) – \(w^{max}\)
get_parameter_names()[source]

Returns the parameter names

Return type:iterable(str)
get_parameters_sdram_usage_in_bytes(n_synapse_types, n_weight_terms)[source]

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)[source]

Determine if this weight dependence is the same as another

Parameters:weight_dependence (AbstractWeightDependence) –
Return type:bool
vertex_executable_suffix

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

Return type:str
w_max

\(w^{max}\)

Return type:float
w_min

\(w^{min}\)

Return type:float
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, weight_scales, n_weight_terms)[source]

Write the parameters of the rule to the spec

Parameters:
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)[source]

Bases: spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.abstract_has_a_plus_a_minus.AbstractHasAPlusAMinus, spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.abstract_weight_dependence.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) – \(A_3^-\)
A3_minus

\(A_3^-\)

Return type:float
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()[source]

Returns the parameter names

Return type:iterable(str)
get_parameters_sdram_usage_in_bytes(n_synapse_types, n_weight_terms)[source]

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)[source]

Determine if this weight dependence is the same as another

Parameters:weight_dependence (AbstractWeightDependence) –
Return type:bool
vertex_executable_suffix

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

Return type:str
w_max

\(w^{max}\)

Return type:float
w_min

\(w^{min}\)

Return type:float
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, weight_scales, n_weight_terms)[source]

Write the parameters of the rule to the spec

Parameters: