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Sensory Processing and Information Theory

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From Statistical Physics to Statistical Inference and Back

Part of the book series: NATO ASI Series ((ASIC,volume 428))

Abstract

Biological sensory processing systems are exquisitely complex and varied. Nonetheless, optimization principles and methods rooted in information theory can be used to understand and to make predictions concerning certain aspects of sensory processing. A brief overview of some work in this field is presented. A particular principle, that of ‘maximum information preservation,’ states that a sensory system should preserve as much information as possible at each processing stage, in the presence of noise and subject to various constraints. This optimization principle is applied to a couple of model systems to illustrate how the principle generates ordered maps and processing units (filters) whose properties are similar to those found in biological systems, as well as being useful for constructing artificial learning networks.

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Author information

Authors and Affiliations

  1. IBM T. J. Watson Research Center, Yorktown Heights, New York, 10546

    Ralph Linsker

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  1. Ralph Linsker
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Editor information

Editors and Affiliations

  1. Department of Theoretical Physics, University of Wuppertal, Wuppertal, Germany

    Peter Grassberger

  2. Laboratory of Statistical Physics, École Normale Supérieure, Paris, France

    Jean-Pierre Nadal

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© 1994 Springer Science+Business Media Dordrecht

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Linsker, R. (1994). Sensory Processing and Information Theory. In: Grassberger, P., Nadal, JP. (eds) From Statistical Physics to Statistical Inference and Back. NATO ASI Series, vol 428. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-1068-6_15

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  • DOI: https://doi.org/10.1007/978-94-011-1068-6_15

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-4465-3

  • Online ISBN: 978-94-011-1068-6

  • eBook Packages: Springer Book Archive

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