Source code for norse.torch.functional.lif_box

"""
A simplified version of the popular leaky integrate-and-fire neuron model that combines a :mod:`norse.torch.functional.leaky_integrator` with spike thresholds to produce events (spikes).
Compared to the :mod:`norse.torch.functional.lif` modules, this model leaves out the current term, making it computationally simpler but impossible to implement in physical systems because currents cannot "jump" in nature.
It is these sudden current jumps that gives the model its name, because the shift in current is instantaneous and can be drawn as "current boxes".
"""

from typing import NamedTuple, Tuple
import torch
import torch.jit

from norse.torch.functional.threshold import threshold
from norse.torch.functional.reset import ResetMethod, reset_value


[docs] class LIFBoxParameters(NamedTuple): """Parametrization of a boxed LIF neuron Parameters: tau_mem_inv (torch.Tensor): inverse membrane time constant (:math:`1/\\tau_\\text{mem}`) in 1/ms v_leak (torch.Tensor): leak potential in mV v_th (torch.Tensor): threshold potential in mV v_reset (torch.Tensor): reset potential in mV method (str): method to determine the spike threshold (relevant for surrogate gradients) alpha (float): hyper parameter to use in surrogate gradient computation """ tau_mem_inv: torch.Tensor = torch.as_tensor(1.0 / 1e-2) v_leak: torch.Tensor = torch.as_tensor(0.0) v_th: torch.Tensor = torch.as_tensor(1.0) v_reset: torch.Tensor = torch.as_tensor(0.0) method: str = "super" alpha: float = torch.as_tensor(100.0) reset_method: ResetMethod = reset_value
class LIFBoxState(NamedTuple): """State of a LIF neuron Parameters: z (torch.Tensor): recurrent spikes v (torch.Tensor): membrane potential """ z: torch.Tensor v: torch.Tensor
[docs] class LIFBoxFeedForwardState(NamedTuple): """State of a feed forward LIF neuron Parameters: v (torch.Tensor): membrane potential """ v: torch.Tensor
[docs] def lif_box_feed_forward_step( input_tensor: torch.Tensor, state: LIFBoxFeedForwardState, p: LIFBoxParameters = LIFBoxParameters(), dt: float = 0.001, ) -> Tuple[torch.Tensor, LIFBoxFeedForwardState]: # pragma: no cover r"""Computes a single euler-integration step for a lif neuron-model without current terms. It takes as input the input current as generated by an arbitrary torch module or function. More specifically it implements one integration step of the following ODE .. math:: \dot{v} = 1/\tau_{\text{mem}} (v_{\text{leak}} - v + i) together with the jump condition .. math:: z = \Theta(v - v_{\text{th}}) and transition equations .. math:: v = (1-z) v + z v_{\text{reset}} Parameters: input_tensor (torch.Tensor): the input spikes at the current time step state (LIFBoxFeedForwardState): current state of the LIF neuron p (LIFBoxParameters): parameters of a leaky integrate and fire neuron dt (float): Integration timestep to use """ # compute voltage updates dv = dt * p.tau_mem_inv * (input_tensor + p.v_leak - state.v) v_decayed = state.v + dv # compute new spikes z_new = threshold(v_decayed - p.v_th, p.method, p.alpha) # compute reset v_new = p.reset_method(z_new, v_decayed, p.v_reset, p.v_th) return z_new, LIFBoxFeedForwardState(v=v_new)