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A Network-Based Computational Model with Learning

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Unconventional Computation (UC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6079))

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Abstract

As is well-known, a natural neuron is made up of a huge number of biomolecules from a nanoscopic point of view. A conventional ‘artificial neural network’ (ANN) [1] consists of nodes with static functions, but a more realistic model for the brain could be implemented with functional molecular agents which move around the neural network and cause a change in the neural functionality.

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References

  1. Haykin, S.: Neural networks and learning machines. Prentice-Hall, Englewood Cliffs (2009)

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Author information

Authors and Affiliations

  1. National Institute of Information and Communications Technology, 588-2, Iwaoka, Iwaoka-cho, Nishi-ku, Kobe, 651-2492, Japan

    Hideaki Suzuki & Hidefumi Sawai

  2. Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan

    Hiroyuki Ohsaki

Authors
  1. Hideaki Suzuki
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  2. Hiroyuki Ohsaki
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  3. Hidefumi Sawai
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Editor information

Editors and Affiliations

  1. Department of Computer Science, The University of Auckland, Science Centre, 38 Princes Street, 1142, Auckland, New Zealand

    Cristian S. Calude

  2. Graduate School of Information Science and Technology, Department of Computer Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, 113-8656, Tokyo, Japan

    Masami Hagiya

  3. Graduate School of Engineering, Department of Information Engineering, Hiroshima University, 739-8527, Higashi-Hiroshima, Japan

    Kenichi Morita

  4. Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Niels Bohrweg 1, 2333, Leiden, CA, The Netherlands

    Grzegorz Rozenberg

  5. Department of Computer Science and Department of Electronics, University of York, YO10 5DD, Heslington, York, UK

    Jon Timmis

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© 2010 Springer-Verlag Berlin Heidelberg

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Suzuki, H., Ohsaki, H., Sawai, H. (2010). A Network-Based Computational Model with Learning. In: Calude, C.S., Hagiya, M., Morita, K., Rozenberg, G., Timmis, J. (eds) Unconventional Computation. UC 2010. Lecture Notes in Computer Science, vol 6079. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13523-1_26

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  • DOI: https://doi.org/10.1007/978-3-642-13523-1_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13522-4

  • Online ISBN: 978-3-642-13523-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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