python_models8.neuron.plasticity.stdp.timing_dependence package

Submodules

python_models8.neuron.plasticity.stdp.timing_dependence.my_timing_dependence module

class python_models8.neuron.plasticity.stdp.timing_dependence.my_timing_dependence.MyTimingDependence(my_potentiation_parameter, my_depression_parameter, A_plus=0.01, A_minus=0.01)[source]

Bases: AbstractTimingDependence

Parameters:

synapse_structure – The synaptic structure of the plastic part of the rows.

property A_minus

\(A^-\)

Return type:

float

property A_plus

\(A^+\)

Return type:

float

NUM_PARAMETERS = 2
get_parameter_names() → List[str][source]

Get the parameter names available from the component.

Return type:

iterable(str)

get_parameters_sdram_usage_in_bytes() → int[source]

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

Return type:

int

is_same_as(timing_dependence: AbstractTimingDependence) → bool[source]

Determine if this timing dependence is the same as another.

Parameters:

timing_dependence (AbstractTimingDependence)

Return type:

bool

property my_depression_parameter
property my_potentiation_parameter
property n_weight_terms

The number of weight terms expected by this timing rule

property pre_trace_n_bytes

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

property vertex_executable_suffix

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

write_parameters(spec: DataSpecificationBase, global_weight_scale: float, synapse_weight_scales: ndarray[tuple[int, ...], dtype[floating]]) → None[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

Module contents