python_models8.neuron.plasticity.stdp.weight_dependence package

Submodules

python_models8.neuron.plasticity.stdp.weight_dependence.my_weight_dependence module

class python_models8.neuron.plasticity.stdp.weight_dependence.my_weight_dependence.MyWeightDependence(w_min=0.0, w_max=1.0, my_weight_parameter=0.1)[source]

Bases: AbstractHasAPlusAMinus, AbstractWeightDependence

WORDS_PER_SYNAPSE_TYPE = 3
get_parameter_names() → List[str][source]

Get the parameter names available from the component.

Return type:

iterable(str)

get_parameters_sdram_usage_in_bytes(n_synapse_types: int, n_weight_terms: int) → int[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: AbstractWeightDependence) → bool[source]

Determine if this weight dependence is the same as another.

Parameters:

weight_dependence (AbstractWeightDependence)

Return type:

bool

property my_weight_parameter
property vertex_executable_suffix

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

property w_max
property w_min
property weight_maximum

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

write_parameters(spec: DataSpecificationBase, global_weight_scale: float, synapse_weight_scales: ndarray[tuple[int, ...], dtype[floating]], n_weight_terms: int)[source]

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

Module contents