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Loading Temporal Associative Memory Using the Neuronic Equation

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Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003 (ICANN 2003, ICONIP 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2714))

Abstract

We discuss the loading capacity of the neuronic equation for temporal associative memory. We show explicitly how to synthesize a perfect temporal associative memory using a network of such neurons, where all non-linear aspects can be linearized in tensorial space.

Supported by National Science Council NSC 91-2213-E-002-124

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Author information

Authors and Affiliations

  1. Dept. of Computer Science and Information Engineering, Taiwan

    Cheng-Yuan Liou

  2. Dept. of Physics, National Taiwan University, Taiwan

    Un-Cheong Sou

Authors
  1. Cheng-Yuan Liou
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  2. Un-Cheong Sou
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Editor information

Editors and Affiliations

  1. Bogazici University, Bebek, 34342, Istanbul, Turkey

    Okyay Kaynak  & Ethem Alpaydin  & 

  2. Laboratory of Computer and Information Science, Helsinki University of Technology, P.O.B. 5400, 02015, Finland

    Erkki Oja

  3. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong

    Lei Xu

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© 2003 Springer-Verlag Berlin Heidelberg

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Liou, CY., Sou, UC. (2003). Loading Temporal Associative Memory Using the Neuronic Equation. In: Kaynak, O., Alpaydin, E., Oja, E., Xu, L. (eds) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. ICANN ICONIP 2003 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44989-2_7

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  • DOI: https://doi.org/10.1007/3-540-44989-2_7

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40408-8

  • Online ISBN: 978-3-540-44989-8

  • eBook Packages: Springer Book Archive

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