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Neural Computation, Vol 8, 319-339, Copyright © 1996 by The MIT Press

ARTICLES

Binary-oscillator networks: bridging a gap between experimental and abstract modeling of neural networks

WP Wang
Department of Mathematics, University of North Carolina, Chapel Hill 27599, USA.

This paper proposes a simplified oscillator model, called binary- oscillator, and develops a class of neural network models having binary- oscillators as basic units. The binary-oscillator has a binary dynamic variable v = +/- 1 modeling the "membrane potential" of a neuron, and due to the presence of a "slow current" (as in a classical relaxation- oscillator) it can oscillate between two states. The purpose of the simplification is to enable abstract algorithmic study on the dynamics of oscillator networks. A binary-oscillator network is formally analogous to a system of stochastic binary spins (atomic magnets) in statistical mechanics.




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J COGNITIVE NEUROSCIENCE NEURAL COMPUTATION MIT PRESS JOURNALS
Copyright © 1996 by The MIT Press.