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Learning to Generalize from Single Examples in Dynamic Link Architecture

  • Conference paper
Temporal Coding in the Brain

Part of the book series: Research and Perspectives in Neurosciences ((NEUROSCIENCE))

Abstract

A large attraction of neural systems lies in their promise of replacing programming by learning. A problem with many current neural models is that, with realistically large input patterns, learning time explodes. This is a problem inherent in a notion of learning that is based almost entirely on statistical estimation. We propose here a different learning style, wherein significant relations in the input patterns are recognized and expressed by the unsupervised self-organization of dynamic links. The power of this mechanism is due to the very general a prioriprinciple of conservation of topological structure. We demonstrate that style with a system that learns to classify mirror symmetric pixel patterns from single examples.

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Authors
  1. W. Konen
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  2. C. von der Malsburg
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Editor information

Editors and Affiliations

  1. Center for Molecular and Behavioral Neuroscience, Rutgers University, 197 University Avenue, Newark, NJ, 07102, USA

    G. Buzsáki Ph. D.

  2. Department of Physiology and Biophysics, New York University Medical Center, 550 First Avenue, New York, NY, 10016, USA

    R. Llinás Ph.D.

  3. Max Planck Institute for Brain Research, Deutschordenstr. 46, D-60528, Frankfurt/Main, Germany

    W. Singer Ph.D.

  4. Laboratoire de Physiologie de la Perception et de l’Action, Collège de France-CNRS, 15, rue de l’Ecole de Medecine, F-75270, Paris Cedex 06, France

    A. Berthoz Ph. D.

  5. Fondation IPSEN, 24, rue Erlanger, F-75016, Paris, France

    Y. Christen Ph. D.

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© 1994 Springer-Verlag Berlin Heidelberg

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Konen, W., von der Malsburg, C. (1994). Learning to Generalize from Single Examples in Dynamic Link Architecture. In: Buzsáki, G., Llinás, R., Singer, W., Berthoz, A., Christen, Y. (eds) Temporal Coding in the Brain. Research and Perspectives in Neurosciences. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-85148-3_12

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  • DOI: https://doi.org/10.1007/978-3-642-85148-3_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-85150-6

  • Online ISBN: 978-3-642-85148-3

  • eBook Packages: Springer Book Archive

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