Chaos in Neural Networks Composed of Coincidence Detector Neurons

Watanabe, M

doi:10.1016/S0893-6080(97)00037-3

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Chaos in Neural Networks Composed of Coincidence Detector Neurons

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Watanabe, M
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Watanabe, M. (1997). Chaos in Neural Networks Composed of Coincidence Detector Neurons. Neural Networks, 10(8), 1353-1359. doi:10.1016/S0893-6080(97)00037-3.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-E9AE-6

Abstract

Chaotic behaviour is observed in neural network models composed of coincidence detector neurons. We use a continuous time and deterministic point process model with uniform synaptic strength and random delay, and apply periodical external inputs to a few neurons in the network. We show that the network dynamics becomes chaotic when the length of “chain firing” starting from an external input becomes practically infinite.