spark.nn.neurons.alif

Contents

spark.nn.neurons.alif#

Classes#

`ALIFNeuronConfig`	ALIFNeuron configuration class.
`ALIFNeuron`	Leaky integrate and fire neuronal model.

Module Contents#

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

Bases: spark.nn.neurons.NeuronConfig

ALIFNeuron configuration class.

Parameters:: __skip_validation__ (bool)

inhibitory_rate: float[source]#

soma: spark.nn.components.somas.leaky.AdaptiveLeakySomaConfig[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.alif.ALIFNeuron(config=None, **kwargs)[source]#

Bases: spark.nn.neurons.Neuron

Leaky integrate and fire neuronal model.

Parameters:: config (ALIFNeuronConfig | None)

config: ALIFNeuronConfig[source]#

soma: spark.nn.components.somas.leaky.AdaptiveLeakySoma[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