norse.torch.functional.lif_feed_forward_step#

norse.torch.functional.lif_feed_forward_step(input_spikes: Tensor, state: LIFFeedForwardState, p: LIFParameters, dt: float = 0.001) → Tuple[Tensor, LIFFeedForwardState][source]#

Computes a single euler-integration step for a lif neuron-model. 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

$$\begin{array}{r}\begin{array}{rl}\dot{v}& =1/{\tau }_{\text{mem}}(v_{\text{leak}}-v+i)\\ \dot{i}& =-1/{\tau }_{\text{syn}}i\end{array}\end{array}$$

together with the jump condition

$$z=\mathrm{\Theta }(v-v_{\text{th}})$$

and transition equations

$$\begin{array}{r}\begin{array}{rl}v& =(1-z)v+z{v}_{\text{reset}}\\ i& =i+i_{\text{in}}\end{array}\end{array}$$

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

Parameters:

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