norse.torch.functional.lif_ex.lif_ex_feed_forward_step#

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 https://neuronaldynamics.epfl.ch/online/Ch5.S2.html. 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

$$\begin{array}{r}\begin{array}{rl}\dot{v}& =1/{\tau }_{\text{mem}}(v_{\text{leak}}-v+i+{\mathrm{\Delta }}_{T}exp\left(\frac{v-v_{\text{th}}}{{\mathrm{\Delta }}_T}\right))\\ \dot{i}& =-1/{\tau }_{\text{syn}}i\end{array}\end{array}$$

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_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