Michael Crair, William Bialek
We study networks of spiking neurons in which spikes are fired as a Poisson process. The state of a cell is determined by the instan(cid:173) taneous firing rate, and in the limit of high firing rates our model reduces to that studied by Hopfield. We find that the inclusion of spiking results in several new features, such as a noise-induced asymmetry between "on" and "off" states of the cells and probabil(cid:173) ity currents which destroy the usual description of network dynam(cid:173) ics in terms of energy surfaces. Taking account of spikes also al(cid:173) lows us to calibrate network parameters such as "synaptic weights" against experiments on real synapses. Realistic forms of the post synaptic response alters the network dynamics, which suggests a novel dynamical learning mechanism.