title: Chaos in Neural Networks Composed of Coincidence Detector Neurons.
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. Copyright 1997 Elsevier Science Ltd.
authors.affiliation: Department of Mathematical Engineering and Information Physics, School of Engineering, The University of Tokyo, Japan
authors.forename: Kazuyuki
authors.initials: K
authors.lastname: Aihara
authors.forename: Masataka
authors.initials: M
authors.lastname: Watanabe