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Modeling and complexity in neural networks

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Abstract

In this paper, we study nonlinear spatio-temporal dynamics in synchronous and asynchronous chaotic neural networks from the viewpoint of the modeling and complexity of the dynamic brain. First, the possible roles and functions of spatio-temporal neurochaos are considered with a model of synchronous chaotic neural networks composed of a neuron model with a chaotic map. Second, deterministic point-process dynamics with spikes of action potentials is demonstrated with a biologically more plausible model of asynchronous chaotic neural networks. Last, the possibilities of inventing a new brain-type of computing system are discussed on the basis of these models of chaotic neural networks.

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Authors and Affiliations

  1. Department of Mathematical Engineering and Information Physics, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, 113-8656, Tokyo, Japan

    Kazuyuki Aihara & Natsuhiro Ichinose

Authors
  1. Kazuyuki Aihara
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  2. Natsuhiro Ichinose
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Correspondence to Kazuyuki Aihara.

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Aihara, K., Ichinose, N. Modeling and complexity in neural networks. Artif Life Robotics 3, 148–154 (1999). https://doi.org/10.1007/BF02481131

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  • DOI: https://doi.org/10.1007/BF02481131

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