norse.torch.functional.lif_ex.lif_ex_feed_forward_step(input_spikes: Tensor, state: LIFExFeedForwardState = (0, 0), p: LIFExParameters = (tensor(0.5000), tensor(200.), tensor(100.), tensor(0.), tensor(1.), tensor(0.), 'super', 100.0), dt: float = 0.001) Tuple[Tensor, LIFExFeedForwardState][source]#

Computes a single euler-integration step of an exponential LIF neuron-model adapted from 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

\[\begin{split}\begin{align*} \dot{v} &= 1/\tau_{\text{mem}} \left(v_{\text{leak}} - v + i + \Delta_T exp\left({{v - v_{\text{th}}} \over {\Delta_T}}\right)\right) \\ \dot{i} &= -1/\tau_{\text{syn}} i \end{align*}\end{split}\]

together with the jump condition

\[z = \Theta(v - v_{\text{th}})\]

and transition equations

\[\begin{split}\begin{align*} v &= (1-z) v + z v_{\text{reset}} \\ i &= i + i_{\text{in}} \end{align*}\end{split}\]

where \(i_{\text{in}}\) is meant to be the result of applying an arbitrary pytorch module (such as a convolution) to input spikes.


input_spikes (torch.Tensor): the input spikes at the current time step state (LIFExFeedForwardState): current state of the LIF neuron p (LIFExParameters): parameters of a leaky integrate and fire neuron dt (float): Integration timestep to use