jaxsnn.event.stepping

Modules

`jaxsnn.event.stepping.step`(dynamics, …)	Find next spike (external or internal), and simulate to that point.
`jaxsnn.event.stepping.step_existing_events`
`jaxsnn.event.stepping.types`

Functions

jaxsnn.event.stepping.step(dynamics: Callable, tr_dynamics: Callable, t_max: float, solver: Callable[[jaxsnn.event.types.LIFState, float, float], jaxsnn.event.types.Spike], step_input: Tuple[jaxsnn.event.types.StepState, Union[jaxsnn.event.types.WeightInput, jaxsnn.event.types.WeightRecurrent], int], *args: int) → Tuple[Tuple[jaxsnn.event.types.StepState, Union[jaxsnn.event.types.WeightInput, jaxsnn.event.types.WeightRecurrent], int], jaxsnn.event.types.EventPropSpike]

Find next spike (external or internal), and simulate to that point.

Args:: dynamics (Callable): Function describing the continuous neuron dynamics tr_dynamics (Callable): Function describing the transition dynamics t_max (float): Max time until which to run solver (Solver): Parallel root solver which returns the next event state (StepInput): (StepState, weights, int)
Returns:: Tuple[StepInput, Spike]: New state after transition and stored spike

jaxsnn.event.stepping.step_existing(dynamics: Callable, tr_dynamics: Callable, t_max: float, event_stepper: Callable[[jaxsnn.event.types.LIFState, float, float], jaxsnn.event.types.Spike], step_input: Tuple[jaxsnn.event.types.StepState, Union[jaxsnn.event.types.WeightInput, jaxsnn.event.types.WeightRecurrent], int], *args: int) → Tuple[Tuple[jaxsnn.event.types.StepState, Union[jaxsnn.event.types.WeightInput, jaxsnn.event.types.WeightRecurrent], int], jaxsnn.event.types.EventPropSpike]

Find next spike (external or internal), and simulate to that point.

Parameters

dynamics – Function describing the continuous neuron dynamics
tr_dynamics – Function describing the transition dynamics
t_max – Max time until which to run
solver – Parallel root solver which returns the next event
state – (StepState, weights, int)

Returns

New state after transition and stored spike