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Chaotic Dynamics in Neural Pattern Recognition

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From Statistics to Neural Networks

Part of the book series: NATO ASI Series ((NATO ASI F,volume 136))

Abstract

The use of impulse inputs for characterizing neural “signals” that are embedded in electroencephalographic EEG “noise” is reviewed in the context of linear systems analysis. Examples are given of the use of linear and nearly linear basis functions with statistics for measurement of neural activity patterns of single cells and populations, with emphasis on the relations between microscopic and macroscopic activity, including measurement of local mean fields and description of the transfer of microscopic sensory information to the macroscopic level of perception and then back to microscopic cortical output by action potentials. Some elements of nonlinear dynamics are introduced that are needed to understand the emergence of low-dimensional aperiodic activity in sensory cortex, and its possible uses for the operation of Hebbian synapses during the learning of new generalization gradients.

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Author information

Authors and Affiliations

  1. Division of Neurobiology, Department of Molecular & Cell Biology, University of California at Berkeley, CA, 94720, USA

    Walter J. Freeman

Authors
  1. Walter J. Freeman
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Minnesota, Minneapolis, MN, 55455, USA

    Vladimir Cherkassky

  2. Department of Statistics, Stanford University, Stanford, CA, 94309, USA

    Jerome H. Friedman

  3. Computer Science Department, George Mason University, Fairfax, VA, 22030, USA

    Harry Wechsler

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© 1994 Springer-Verlag Berlin Heidelberg

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Freeman, W.J. (1994). Chaotic Dynamics in Neural Pattern Recognition. In: Cherkassky, V., Friedman, J.H., Wechsler, H. (eds) From Statistics to Neural Networks. NATO ASI Series, vol 136. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-79119-2_18

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  • DOI: https://doi.org/10.1007/978-3-642-79119-2_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-79121-5

  • Online ISBN: 978-3-642-79119-2

  • eBook Packages: Springer Book Archive

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