jaxsnn.event.stepping.step_existing_events

Classes

EventPropSpike(time, idx, current)

StepState(neuron_state, spike_times, …)

Functions

jaxsnn.event.stepping.step_existing_events.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