spark.nn.components.delays.n_delays

Classes

NDelaysConfig	NDelays configuration class.
NDelays	Data structure for spike storage and retrival for efficient neuron spike delay implementation.

Module Contents

class spark.nn.components.delays.n_delays.NDelaysConfig(__skip_validation__=False, **kwargs)

Bases: spark.nn.components.delays.base.DelaysConfig

NDelays configuration class.

Parameters:

__skip_validation__ (bool)

max_delay: float

delays: jax.Array | spark.nn.initializers.base.Initializer

class spark.nn.components.delays.n_delays.NDelays(config=None, **kwargs)

Bases: spark.nn.components.delays.base.Delays

Data structure for spike storage and retrival for efficient neuron spike delay implementation. This synaptic delay model implements a generic conduction delay of the outputs spikes of neruons. Example: Neuron A fires, every neuron that listens to A recieves its spikes K timesteps later,

neuron B fires, every neuron that listens to B recieves its spikes L timesteps later.

Init:

max_delay: float delay_initializer: DelayInitializerConfig

Input:

in_spikes: SpikeArray

Output:

out_spikes: SpikeArray

Parameters:

config (NDelaysConfig)

config: NDelaysConfig

build(input_specs)

Build method.

Parameters:

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

reset()

Resets component state.

Return type:

None

get_dense()

Convert bitmask to dense vector (aligned with MSB-first packing).

Return type:

jax.Array

__call__(in_spikes)

Execution method.

Parameters:

in_spikes (spark.core.payloads.SpikeArray)

Return type:

spark.nn.components.delays.base.DelaysOutput