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Self-organization Dynamics in Chaotic Neural Networks

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Control and Chaos

Part of the book series: Mathematical Modelling ((MMO,volume 8))

Abstract

We examine roles of deterministic chaos in artificial neural networks with self-organization dynamics which has the ability to switch automatically between learning and retrieving modes in accordance with the given input pattern. We demonstrate that the chaotic dynamics works as means to learn new patterns and increases the memory capacity of the neural network.

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Author information

Authors and Affiliations

  1. Department of Quantum Engineering and Systems Science, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113, Japan

    Masataka Watanabe & Shunsuke Kondo

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

    Kazuyuki Aihara

Authors
  1. Masataka Watanabe
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  2. Kazuyuki Aihara
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  3. Shunsuke Kondo
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Editor information

Editors and Affiliations

  1. Center for Applied Dynamics and Optimization, Department of Mathematics, The University of Western Australia, 6907, Perth, Australia

    Kevin Judd , Alistair Mees  & Kok Lay Teo ,  & 

  2. Aerospace and Mechanical Engineering, University of Arizona, 85721, Tucson, Arizona, USA

    Thomas L. Vincent

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© 1997 Birkhäuser Boston

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Watanabe, M., Aihara, K., Kondo, S. (1997). Self-organization Dynamics in Chaotic Neural Networks. In: Judd, K., Mees, A., Teo, K.L., Vincent, T.L. (eds) Control and Chaos. Mathematical Modelling, vol 8. Birkhäuser Boston. https://doi.org/10.1007/978-1-4612-2446-4_20

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  • DOI: https://doi.org/10.1007/978-1-4612-2446-4_20

  • Publisher Name: Birkhäuser Boston

  • Print ISBN: 978-1-4612-7540-4

  • Online ISBN: 978-1-4612-2446-4

  • eBook Packages: Springer Book Archive

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