This is an AI question regarding 3rd generation neural networks - spiking neural networks (SNN).I hve been studying this concept online from various papers, mainly Maass (1997). I and am not entirely sure I understand why SNN's are considered pulse-code in contrast to earlier ANN's which are rate-code. I have background in neuroscience so I understand the terms and ratio, I am asking regarding the actual implementation. Is the practical difference in the fact that when each neuron updates its current state in an SNN it deals with the entire history of every pre-synaptic neuron and not only the last step? Is that what gives it temporal characteristics which previous generation ANN's lack? What is the key computational difference between SNN and earlier approaches?NotesThis question was migrated from SO, and a duplicate was asked on cstheory that was migrated to CS.SE.
Computational differences between spiking neural networks and previous ANNs
computational modeling;neural network;artificial intelligence