jaxsnn.discrete.modules.lif

Classes

DenseData	alias of jax.Array
LIFParameters(tau_syn, tau_mem, v_th, …)
LIFState(V, I)
Parameter	alias of jax.Array
Population(generator, …)	A population module for the discrete paradigm.

Functions

jaxsnn.discrete.modules.lif.LIF(size, params: jaxsnn.discrete.functional.lif.LIFParameters = LIFParameters(tau_syn=0.005, tau_mem=0.01, v_th=0.6, v_leak=0.0, v_reset=0.0), method: Callable = <jax._src.custom_derivatives.custom_vjp object>) → jaxsnn.discrete.types.Population

Layer constructor function for a leaky-integrate and fire layer.

Parameters

• size – Number of neurons in the layer.

• params – Parameters for the LIF neuron model.

• method – Surrogate gradient method for the threshold function.

Returns

A Population object containing the layer definition.

jaxsnn.discrete.modules.lif.lif_step(inputs: Dict[str, jax.Array], state: jaxsnn.discrete.functional.lif.LIFState, parameters: Optional[jax.Array, None], method: Callable, v_leak: float, v_th: float, v_reset: float, tau_mem: float, tau_syn: float, dt: float = 0.001) → Tuple[jaxsnn.discrete.functional.lif.LIFState, jax.Array]

Euler step of a leaky-integrate-and-fire neuron.

Parameters

• inputs – Dictionary of input currents

• state – Current neuron state

• parameters – Optional learnable parameters

• method – Surrogate gradient method for the threshold function

• v_leak – Leak potential

• v_th – Threshold potential

• v_reset – Reset potential

• tau_mem – Membrane time constant

• tau_syn – Synaptic time constant

• dt – Time step size

Returns

Tuple of updated neuron state and membrane potential