spark.nn.interfaces.input.topological

spark.nn.interfaces.input.topological#

Classes#

`TopologicalSpikerConfig`	Base TopologicalSpiker configuration class.
`TopologicalPoissonSpikerConfig`	TopologicalPoissonSpiker configuration class.
`TopologicalPoissonSpiker`	Transforms a continuous signal to a spiking signal.
`TopologicalLinearSpikerConfig`	TopologicalLinearSpiker configuration class.
`TopologicalLinearSpiker`	Transforms a continuous signal to a spiking signal.

Module Contents#

class spark.nn.interfaces.input.topological.TopologicalSpikerConfig(__skip_validation__=False, **kwargs)[source]#

Bases: spark.nn.interfaces.input.base.InputInterfaceConfig

Base TopologicalSpiker configuration class.

Parameters:: __skip_validation__ (bool)

glue: jax.Array[source]#

mins: jax.Array[source]#

maxs: jax.Array[source]#

resolution: int[source]#

sigma: float[source]#

class spark.nn.interfaces.input.topological.TopologicalPoissonSpikerConfig(__skip_validation__=False, **kwargs)[source]#

Bases: TopologicalSpikerConfig, spark.nn.interfaces.input.poisson.PoissonSpikerConfig

TopologicalPoissonSpiker configuration class.

Parameters:: __skip_validation__ (bool)

class spark.nn.interfaces.input.topological.TopologicalPoissonSpiker(config=None, **kwargs)[source]#

Bases: spark.nn.interfaces.input.base.InputInterface

Transforms a continuous signal to a spiking signal. This transformation maps a vector (a point in a hypercube) into a simple manifold with/without its borders glued. This transformation assumes a very simple poisson neuron model without any type of adaptation or plasticity.

Init:: glue: jax.Array mins: jax.Array maxs: jax.Array resolution: int max_freq: float [Hz] sigma: float
Input:: signal: FloatArray
Output:: spikes: SpikeArray

Parameters:: config (TopologicalPoissonSpikerConfig | None)

config: TopologicalPoissonSpikerConfig[source]#

resolution[source]#

max_freq[source]#

sigma[source]#

build(input_specs)[source]#

Build method.

Parameters:: input_specs (dict[str, spark.core.specs.PortSpecs])
Return type:: None

__call__(signal)[source]#

Input interface operation.

Input: A FloatArray of values in the range [mins, maxs]. Output: A SpikeArray of the same shape as the input.

Parameters:: signal (spark.core.payloads.FloatArray)
Return type:: spark.nn.interfaces.input.base.InputInterfaceOutput

class spark.nn.interfaces.input.topological.TopologicalLinearSpikerConfig(__skip_validation__=False, **kwargs)[source]#

Bases: TopologicalSpikerConfig, spark.nn.interfaces.input.linear.LinearSpikerConfig

TopologicalLinearSpiker configuration class.

Parameters:: __skip_validation__ (bool)

class spark.nn.interfaces.input.topological.TopologicalLinearSpiker(config=None, **kwargs)[source]#

Bases: spark.nn.interfaces.input.base.InputInterface

Transforms a continuous signal to a spiking signal. This transformation maps a vector (a point in a hypercube) into a simple manifold with/without its borders glued. This transformation assumes a very simple linear neuron model without any type of adaptation or plasticity.

Init:: glue: jax.Array mins: jax.Array maxs: jax.Array resolution: int tau: float [ms] cd: float [ms] max_freq: float [Hz] sigma: float
Input:: signal: FloatArray
Output:: spikes: SpikeArray