spark.nn.neurons.adex

spark.nn.neurons.adex#

Classes#

AdExNeuronConfig

AdExNeuron configuration class.

AdExNeuron

Leaky Integrate and Fire neuronal model.

Module Contents#

class spark.nn.neurons.adex.AdExNeuronConfig(__skip_validation__=False, **kwargs)[source]#

Bases: spark.nn.neurons.NeuronConfig

AdExNeuron configuration class.

Parameters:

__skip_validation__ (bool)

inhibitory_rate: float[source]#
soma: spark.nn.components.somas.exponential.AdaptiveExponentialSomaConfig[source]#
synapses: spark.nn.components.synapses.base.SynanpsesConfig[source]#
delays: spark.nn.components.delays.base.DelaysConfig | None[source]#
learning_rule: spark.nn.components.learning_rules.base.LearningRuleConfig | None[source]#
class spark.nn.neurons.adex.AdExNeuron(config=None, **kwargs)[source]#

Bases: spark.nn.neurons.Neuron

Leaky Integrate and Fire neuronal model.

Parameters:

config (AdExNeuronConfig | None)

config: AdExNeuronConfig[source]#
soma: spark.nn.components.somas.exponential.AdaptiveExponentialSoma[source]#
delays: spark.nn.components.delays.base.Delays[source]#
synapses: spark.nn.components.synapses.base.Synanpses[source]#
learning_rule: spark.nn.components.learning_rules.base.LearningRule[source]#
build(input_specs)[source]#

Build method.

Parameters:

input_specs (dict[str, spark.core.specs.PortSpecs])

__call__(in_spikes)[source]#

Update neuron’s states and compute spikes.

Parameters:

in_spikes (spark.core.payloads.SpikeArray)

Return type:

spark.nn.neurons.NeuronOutput

Contents