Fast Sigmoidal N etworks v ia Spiking Neurons
We show that networks of relatively realistic mathematical models for biological neurons in principle can simulate arbitrary feedfornard sigmoidal neural neb in away that has previoudv not been considered. This new approach is based on temporal coding b;single spikes (mspectively bv the timine of svnchmnous firin= in ~oals of neurons) rather than on ., 2 -. the traditional interpretation of analog variables in terms of firing rates. Ihe resulting new simulation is subrtantially faster and hence more consistent withexperimental resulbabout Ihemuimalspeedof information processing in cortical neural systems. Asa consequence wecan show that networks of noisy spiking neurons are "universal soomximators" in the sense that thev can a~oroximate . . , .. with regard to temporal coding any givencontinuous function of several variables. This result holds for a fairly large class of schemes for coding analog variables by firing times of spiking neurons. This new proposal for the possible organization of computations in nehvorks of spiking neurons systems has some interesting consequences for the type of learning rules that would be needed to explain the selforganization of such networks. Finally, the fast and noise-robust implementation of sigmoidal neural nets by temporal coding points to possible new ways of implementing feedforward and recurrent sigmoidal neural neb with pulse stream VLSI.