spark.nn.components.somas.leaky

spark.nn.components.somas.leaky#

Classes#

`LeakySomaConfig`	LeakySoma model configuration class.
`LeakySoma`	Leaky soma model.
`RefractoryLeakySomaConfig`	RefractoryLeakySoma model configuration class.
`RefractoryLeakySoma`	Leaky soma with refractory time model.
`StrictRefractoryLeakySomaConfig`	StrictRefractoryLeakySoma model configuration class.
`StrictRefractoryLeakySoma`	Leaky soma with strict refractory time model.
`AdaptiveLeakySomaConfig`	AdaptiveLeakySoma model configuration class.
`AdaptiveLeakySoma`	Adaptive leaky soma model.

Module Contents#

class spark.nn.components.somas.leaky.LeakySomaConfig(__skip_validation__=False, **kwargs)[source]#

Bases: spark.nn.components.somas.base.SomaConfig

LeakySoma model configuration class.

Parameters:: __skip_validation__ (bool)

potential_rest: float | jax.Array | spark.nn.initializers.base.Initializer[source]#

potential_reset: float | jax.Array | spark.nn.initializers.base.Initializer[source]#

potential_tau: float | jax.Array | spark.nn.initializers.base.Initializer[source]#

resistance: float | jax.Array | spark.nn.initializers.base.Initializer[source]#

threshold: float | jax.Array | spark.nn.initializers.base.Initializer[source]#

class spark.nn.components.somas.leaky.LeakySoma(config=None, **kwargs)[source]#

Bases: spark.nn.components.somas.base.Soma

Leaky soma model.

Init:: units: tuple[int, …] potential_rest: float | jax.Array potential_reset: float | jax.Array potential_tau: float | jax.Array resistance: float | jax.Array threshold: float | jax.Array
Input:: in_spikes: SpikeArray
Output:: out_spikes: SpikeArray
Reference:: Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition. Gerstner W, Kistler WM, Naud R, Paninski L. Chapter 1.3 Integrate-And-Fire Models https://neuronaldynamics.epfl.ch/online/Ch1.S3.html

Parameters:: config (LeakySomaConfig | None)

config: LeakySomaConfig[source]#

build(input_specs)[source]#

Build method.

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

class spark.nn.components.somas.leaky.RefractoryLeakySomaConfig(__skip_validation__=False, **kwargs)[source]#

Bases: LeakySomaConfig

RefractoryLeakySoma model configuration class.

Parameters:: __skip_validation__ (bool)

cooldown: float | jax.Array | spark.nn.initializers.base.Initializer[source]#

class spark.nn.components.somas.leaky.RefractoryLeakySoma(config=None, **kwargs)[source]#

Bases: LeakySoma

Leaky soma with refractory time model.

Init:: units: tuple[int, …] potential_rest: float | jax.Array potential_reset: float | jax.Array potential_tau: float | jax.Array resistance: float | jax.Array threshold: float | jax.Array cooldown: float | jax.Array
Input:: in_spikes: SpikeArray
Output:: out_spikes: SpikeArray
Reference:: Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition. Gerstner W, Kistler WM, Naud R, Paninski L. Chapter 1.3 Integrate-And-Fire Models https://neuronaldynamics.epfl.ch/online/Ch1.S3.html

Parameters:: config (RefractoryLeakySomaConfig | None)

config: RefractoryLeakySomaConfig[source]#

build(input_specs)[source]#

Build method.

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

reset()[source]#

Resets component state.

Return type:: None

class spark.nn.components.somas.leaky.StrictRefractoryLeakySomaConfig(__skip_validation__=False, **kwargs)[source]#

Bases: RefractoryLeakySomaConfig

StrictRefractoryLeakySoma model configuration class.

Parameters:: __skip_validation__ (bool)

class spark.nn.components.somas.leaky.StrictRefractoryLeakySoma(config=None, **kwargs)[source]#

Bases: RefractoryLeakySoma

Leaky soma with strict refractory time model. Note: This model is here mostly for didactic/historical reasons.

Init:: units: tuple[int, …] potential_rest: float | jax.Array potential_reset: float | jax.Array potential_tau: float | jax.Array resistance: float | jax.Array threshold: float | jax.Array cooldown: float | jax.Array
Input:: in_spikes: SpikeArray
Output:: out_spikes: SpikeArray
Reference:: Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition. Gerstner W, Kistler WM, Naud R, Paninski L. Chapter 1.3 Integrate-And-Fire Models https://neuronaldynamics.epfl.ch/online/Ch1.S3.html

Parameters:: config (RefractoryLeakySomaConfig | None)

config: StrictRefractoryLeakySomaConfig[source]#

class spark.nn.components.somas.leaky.AdaptiveLeakySomaConfig(__skip_validation__=False, **kwargs)[source]#

Bases: RefractoryLeakySomaConfig

AdaptiveLeakySoma model configuration class.

Parameters:: __skip_validation__ (bool)

threshold_tau: float | jax.Array | spark.nn.initializers.base.Initializer[source]#

threshold_delta: float | jax.Array | spark.nn.initializers.base.Initializer[source]#

class spark.nn.components.somas.leaky.AdaptiveLeakySoma(config=None, **kwargs)[source]#

Bases: RefractoryLeakySoma

Adaptive leaky soma model.

Init:: units: tuple[int, …] potential_rest: float | jax.Array potential_reset: float | jax.Array potential_tau: float | jax.Array resistance: float | jax.Array threshold: float | jax.Array cooldown: float | jax.Array threshold_tau: float | jax.Array threshold_delta: float | jax.Array
Input:: in_spikes: SpikeArray
Output:: out_spikes: SpikeArray
Reference:: Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition. Gerstner W, Kistler WM, Naud R, Paninski L. Chapter 5.1 Thresholds in a nonlinear integrate-and-fire model https://neuronaldynamics.epfl.ch/online/Ch5.S1.html

Parameters:: config (AdaptiveLeakySomaConfig | None)

config: AdaptiveLeakySomaConfig[source]#

build(input_specs)[source]#

Build method.

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

reset()[source]#

Resets component state.

Return type:: None