Efficient temporal processing with biologically realistic dynamic synapses

Synapses play a central role in neural computation: the strengths of synapt ic connections determine the function of a neural circuit. In conventional models of computation, synaptic strength is assumed to be a static quantity that changes only on the slow timescale of learning. In biological systems , however, synaptic strength undergoes dynamic modulation on rapid timescal es through mechanisms such as short term facilitation and depression. Here we describe a general model of computation that exploits dynamic synapses, and use a backpropagation-like algorithm to adjust the synaptic parameters. We show that such gradient descent suffices to approximate a given quadrat ic filter by a father small neural system with dynamic synapses. We also co mpare our network model to artificial neural networks designed for time ser ies processing. Our numerical results are complemented by theoretical analy ses which show that even with just a single hidden layer such networks can approximate a surprisingly large class of nonlinear filters: all filters th at can be characterized by Volterra series. This result is robust with rega rd to various changes in the model for synaptic dynamics.