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NeuroCOLT
Technical Report NC-TR-99-039
Dynamic
Stochastic Synapses as Computational Units
Wolfgang Maass,
Institute for Theoretical Computer Science
Technische Universität Graz
Anthony M. Zador
The Salk Institute
La Jolla
California
Received:
22-MAR-1999
Abstract
In most neural network models, synapses are
treated as static weights that change only on 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 pre-synaptic terminal triggers release of a
vesicle of neurotransmitter from a release site with a probability
that can bemuch 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 synapses.
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