Binary-oscillator networks: bridging a gap between experimental and abstract modeling of neural networks.
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.
Wang
W P
WP
Department of Mathematics, University of North Carolina, Chapel Hill 27599, USA.
D000465:Algorithms; D008564:Membrane Potentials / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*