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Learning oscillations using adaptive control

  • Part I: Coding and Learning in Biology
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Artificial Neural Networks — ICANN'97 (ICANN 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1327))

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Abstract

We study a model for learning periodic signals in continouus time Hopfield networks proposed by Doya and Yoshizawa ([3]) that can be considered as a model for temporal pattern memory in animal motor systems. A network receives an external oscillatory input and adjusts its weights so that this signal can be reproduced approximately as the network output after some time. We use tools from adaptive control theory to derive an algorithm for weight matrices with special structure. If the input is generated by a network of the same structure the algorithm converges globally and does not exhibit the deficiencies of the back-propagation based approach of Doya and Yoshizawa.

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Authors and Affiliations

  1. Graduiertenkolleg Technomathematik, Universität Kaiserslautern, Erwin-Schrödinger-Straße, 67663, Kaiserslautern, Germany

    Martin Georg Weiß

Authors
  1. Martin Georg Weiß
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Wulfram Gerstner Alain Germond Martin Hasler Jean-Daniel Nicoud

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© 1997 Springer-Verlag Berlin Heidelberg

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Georg Weiß, M. (1997). Learning oscillations using adaptive control. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020176

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  • DOI: https://doi.org/10.1007/BFb0020176

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63631-1

  • Online ISBN: 978-3-540-69620-9

  • eBook Packages: Springer Book Archive

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