Compute recurrent contribution from spikes

[smonsays]

Hey Friedemann,

thank you for the very comprehensive tutorial! I have a question on the way the recurrence is computed in tutorial 4. If I understand the equation for the dynamics of the current correctly, the recurrence should be computed with the spiking neuron state:

mthr = mem-1.0
out = spike_fn(mthr)
h1 = h1_from_input[:,t] + torch.einsum("ab,bc->ac", (out, v1))

Instead in tutorial 4, a separate hidden state is kept, that ignores the spike function:

h1 = h1_from_input[:,t] + torch.einsum("ab,bc->ac", (h1, v1))

Is this done deliberately? Judging from simulating a few epochs, the two versions seem to perform similarly.

Thank you,

Simon

[fzenke]

Thanks for pointing this out. It looks like a bug.