norse.torch.functional.lif_feed_forward_adjoint_step(input: Tensor, s: LIFFeedForwardState, p: LIFParameters = LIFParameters(tau_syn_inv=tensor(200.), tau_mem_inv=tensor(100.), v_leak=tensor(0.), v_th=tensor(1.), v_reset=tensor(0.), method='super', alpha=tensor(100.)), dt: float = 0.001) Tuple[Tensor, LIFFeedForwardState][source]#

Implementes a single euler forward and adjoint backward step of a leaky integrate and fire neuron with current based exponential synapses.


input (torch.Tensor): input spikes from other cells s (LIFFeedForwardState): state of leaky integrate and fire neuron p (LIFParameters): leaky integrate and fire parameters dt (float): time step of integration