A neural oscillator-network model of temporal pattern generation

Abstract Most contemporary neural network models deal with essentially static, perceptual problems of classification and transformation. Models such as multi-layer feedforward perceptrons generally do not incorporate time as an essential dimension, whereas biological neural networks are inherently temporal systems. In modelling motor behaviour, however, it is essential to have models that are able to produce temporal patterns of varying duration and complexity. A model is proposed, based on a network of pulse oscillators consisting of neuron/interneuron (NiN) pairs. Due to the inherent temporal properties, a simple NiN net, taught by a pseudo-Hebbian learning scheme, could be used in simulating handwriting pen-tip displacement of individual letters.

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