NETWORKS OF SPIKING NEURONS CAN EMULATE ARBITRARY HOPFIELD NETS IN TEMPORAL CODING

A theoretical model for analogue computation in networks of spiking ne urons with temporal coding is introduced and tested through simulation s in GENESIS. It turns out that the use of multiple synapses yields ve ry noise robust mechanisms for analogue computations via the timing of single spikes in networks of detailed compartmental neuron models. In this way, one arrives at a method for emulating arbitrary Hopfield ne ts with spiking neurons in temporal coding, yielding new models fbr as sociative recall of spatio-temporal firing patterns. We also show that it suffices to store these patterns in the efficacies of excitatory s ynapses. A corresponding layered architecture yields a refinement of t he synfire-chain model that can assume a fairly large set of different stable firing patterns for different inputs.