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, however,
that synapses are highly dynamic and show use-dependent plasticity over a
wide range of time scales. Moreover, synaptic transmission is an inherently
stochastic process: a spike arriving at a presynaptic terminal triggers the
release of a vesicle of neurotransmitter from a release site with a
probability 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 interpretations in terms of synaptic physiology. We
investigate the consequences of the model for computing with individual
spikes and demonstrate through rigorous theoretical results that the
computational power of the network is increased through the use of dynamic
Reference: W. Maass and A. M. Zador.
Dynamic stochastic synapses as computational units.
Neural Computation, 11(4):903-917, 1999.