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  Chaos in Neural Networks Composed of Coincidence Detector Neurons

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.

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Watanabe, M1, Author           
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1Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              

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 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.

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 Dates: 1997-11
 Publication Status: Issued
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Title: Neural Networks
Source Genre: Journal
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Pages: - Volume / Issue: 10 (8) Sequence Number: - Start / End Page: 1353 - 1359 Identifier: -