Dynamic stochastic synapses as computational units

In most neural network models, synapses are treated as static weights that change only with the slow time scales of learning. It is well known, howeve r, that synapses are highly dynamic and show use-dependent plasticity over a wide range of time scales. Moreover, synaptic transmission is an inherent ly stochastic process: a spike arriving at a presynaptic terminal triggers the release of a vesicle of neurotransmitter from a release site with a pro bability that can be much less than one. We consider a simple model for dynamic stochastic synapses that can easily be integrated into common models for networks of integrate-and-fire neurons (spiking neurons). The parameters of this model have direct interpretation s in terms of synaptic physiology. We investigate the consequences of the m odel for computing with individual spikes and demonstrate through rigorous theoretical results that the computational power of the network is increase d through the use of dynamic synapses.