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Model Neural Networks and Behavior pp 237–261Cite as

The Logic of Limax Learning

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Abstract

We wish to understand the neuronal computations performed on sensory inputs that result in the categorization of those inputs, their storage as memory states, and their associative combination. Our experimental and theoretical work is focused on the neuronal computations performed on odor and taste inputs in the CNS of Limax maximus, a terrestrial mollusk convenient for behavioral, neuro-physiological, and neurochemical experiments (Gelperin, 1983). The questions posed in this specific system are designed to illuminate issues of learning and memory storage with panphyletic generality.

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Author information

Authors and Affiliations

  1. AT & T Bell Laboratories, Murray Hill, New Jersey, 07974, USA

    Alan Gelperin, J. J. Hopfield & D. W. Tank

  2. Department of Biology, Princeton University, Princeton, New Jersey, 08544, USA

    Alan Gelperin

  3. Divisions of Chemistry and Biology, California Institute of Technology, Pasadena, California, 91125, USA

    J. J. Hopfield

Authors
  1. Alan Gelperin
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  2. J. J. Hopfield
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  3. D. W. Tank
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Editor information

Editors and Affiliations

  1. University of California, San Diego, La Jolla, California, USA

    Allen I. Selverston

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© 1985 Springer Science+Business Media New York

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Gelperin, A., Hopfield, J.J., Tank, D.W. (1985). The Logic of Limax Learning. In: Selverston, A.I. (eds) Model Neural Networks and Behavior. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-5858-0_13

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  • DOI: https://doi.org/10.1007/978-1-4757-5858-0_13

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4757-5860-3

  • Online ISBN: 978-1-4757-5858-0

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