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Dynamics of Cortical Columns – Self-organization of Receptive Fields

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Artificial Neural Networks: Biological Inspirations – ICANN 2005 (ICANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3696))

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Abstract

We present a system of differential equations which abstractly models neural dynamics and synaptic plasticity of a cortical macrocolumn. The equations assume inhibitory coupling between minicolumn activities and Hebbian type synaptic plasticity of afferents to the minicolumns. If input in the form of activity patterns is presented, self-organization of receptive fields (RFs) of the minicolumns is induced. Self-organization is shown to appropriately classify input patterns or to extract basic constituents form input patterns consisting of superpositions of subpatterns. The latter is demonstrated using the bars benchmark test. The dynamics was motivated by the more explicit model suggested in [1] but represents a much compacter, continuous, and easier to analyze dynamic description.

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Author information

Authors and Affiliations

  1. Institut für Neuroinformatik, Ruhr-Universität, 44780, Bochum, Germany

    Jörg Lücke & Jan D. Bouecke

  2. Gatsby Computational Neuroscience Unit, UCL, London, WC1N 3AR, UK

    Jörg Lücke

Authors
  1. Jörg Lücke
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  2. Jan D. Bouecke
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Editor information

Editors and Affiliations

  1. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

    Włodzisław Duch

  2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01–447, Warsaw, Poland

    Janusz Kacprzyk

  3. Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015 HUT, Finland

    Erkki Oja

  4. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Sławomir Zadrożny

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© 2005 Springer-Verlag Berlin Heidelberg

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Lücke, J., Bouecke, J.D. (2005). Dynamics of Cortical Columns – Self-organization of Receptive Fields. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Biological Inspirations – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550822_6

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  • DOI: https://doi.org/10.1007/11550822_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28752-0

  • Online ISBN: 978-3-540-28754-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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