Introduction to the theory of neural computation
Introduction to the theory of neural computation
Learning and relearning in Boltzmann machines
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Alternating and synchronous rhythms in reciprocally inhibitory model neurons
Neural Computation
Compositionality in neural systems
The handbook of brain theory and neural networks
Synchronization in Time-delayed Binary Oscillatory Network
Neural Processing Letters
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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.