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

Submodules

spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.abstract_has_a_plus_a_minus module

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

Bases: object

A_minus
A_plus
set_a_plus_a_minus(a_plus, a_minus)[source]

spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.abstract_weight_dependence module

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

get_provenance_data(pre_population_label, post_population_label)[source]

Get any provenance data

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

the provenance data of the weight dependency
is_same_as(weight_dependence)[source]

Determine if this weight dependence is the same as another

vertex_executable_suffix

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

weight_maximum

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

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

Write the parameters of the rule to the spec

spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.weight_dependence_additive module

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

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

is_same_as(weight_dependence)[source]

Determine if this weight dependence is the same as another

vertex_executable_suffix

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

w_max
w_min
weight_maximum

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

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

Write the parameters of the rule to the spec

spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.weight_dependence_additive_triplet module

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

A3_minus
A3_plus
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

is_same_as(weight_dependence)[source]

Determine if this weight dependence is the same as another

vertex_executable_suffix

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

w_max
w_min
weight_maximum

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

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

Write the parameters of the rule to the spec

spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.weight_dependence_multiplicative module

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

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

is_same_as(weight_dependence)[source]

Determine if this weight dependence is the same as another

vertex_executable_suffix

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

w_max
w_min
weight_maximum

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

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

Write the parameters of the rule to the spec

Module contents

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

Bases: object

A_minus
A_plus
set_a_plus_a_minus(a_plus, a_minus)[source]
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

get_provenance_data(pre_population_label, post_population_label)[source]

Get any provenance data

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

the provenance data of the weight dependency
is_same_as(weight_dependence)[source]

Determine if this weight dependence is the same as another

vertex_executable_suffix

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

weight_maximum

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

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

Write the parameters of the rule to the spec

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

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

is_same_as(weight_dependence)[source]

Determine if this weight dependence is the same as another

vertex_executable_suffix

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

w_max
w_min
weight_maximum

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

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

Write the parameters of the rule to the spec

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

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

is_same_as(weight_dependence)[source]

Determine if this weight dependence is the same as another

vertex_executable_suffix

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

w_max
w_min
weight_maximum

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

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

Write the parameters of the rule to the spec

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

A3_minus
A3_plus
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

is_same_as(weight_dependence)[source]

Determine if this weight dependence is the same as another

vertex_executable_suffix

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

w_max
w_min
weight_maximum

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

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

Write the parameters of the rule to the spec