Networks of spiking neurons can emulate arbitrary Hopfield nets in temporal coding

Abstract

A theoretical model for analogue computation in networks of spiking neurons with temporal coding is introduced and tested through simulations in GENESIS. It turns out that the use of multiple synapses yields very 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 nets with spiking neurons in temporal coding, yielding new models for associative recall of spatio-temporal firing patterns. We also show that it suffices to store these patterns in the efficacies of excitatory synapses.

A corresponding layered architecture yields a refinement of the synfire-chain model that can assume a fairly large set of different stable firing patterns for different inputs.