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Abstract:
In most neural network models, synapses are treated as static
weights that change only on the slow time scales of learning. In
fact, however, 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 release of a vesicle of
neurotransmitter from a release site with a probability that can be
much less than one. Changes in release probability represent one of
the main mechanisms by which synaptic efficacy is modulated in
neural circuits.
We propose and investigate a simple model for stochastic dynamic
synapses that can easily be integrated into common models for
neural computation. We prove through rigorous theoretical analysis
and computer simulations that this model for a stochastic dynamic
synapse can respond with a large variety of different release
patterns to different spike trains, even if they represent the same
firing rate. Furthermore we show that a spiking neuron gains
additional computational power through the use of dynamic synapses,
and we explore new learning issues that arise in this
context.
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