I was going through this discussion of yours ([Nengo DL]: Understanding the internals of Nengo-DL ; Theory and Papers) and I have some fundamental doubts on how spiking networks are learning,
Let’s say I have a basic 3 layer architecture
How the nengoDL converter is learning and predicting for a regression or classification task? is the network is using backpropagation seen in traditional NN or (Hebb’s law, Spike Timing Dependent Plasticity rule)
Is there any biological significance of using the spikingRELU activation function, why not spikingTanh?
I understood the LIF method for the firing of neurons but still not sure how they are helping in prediction.