Mathematical Methods of Neurodynamics and Self-Organization

Shun-ichi Amari
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Shun-ichi Amari: University of Tokyo, Faculty of Engineering

A chapter in Biomathematics and Related Computational Problems, 1988, pp 3-11 from Springer

Abstract: Abstract Information is processed in the brain by parallel mutual interactions of neurons. Moreover, its behavior is improved by self-organization and learning. In order to understand its information processing mechanism, it is necessary to study the properties of the dynamics of neural excitation patterns and of the dynamics of self-organization or learning. Mathematical methods are presented here for analyzing neurodynamics both in local and distributed representations of information.

Keywords: Amplification Factor; Associative Memory; Signal Space; Synaptic Efficacy; Pattern Vector (search for similar items in EconPapers)
Date: 1988
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-94-009-2975-3_1

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DOI: 10.1007/978-94-009-2975-3_1

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