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Chaos, multiplicity, crisis, and synchronicity in higher order neural networks.

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Chaos, multiplicity, crisis, and synchronicity in higher order neural networks.

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dc.contributor.author Wang, Lipo.
dc.contributor.author Ross, John.
dc.date.accessioned 2012-05-23T02:43:51Z
dc.date.available 2012-05-23T02:43:51Z
dc.date.copyright 1991
dc.date.issued 2012-05-23
dc.identifier.citation Wang, L., & Ross, J. (1991). Chaos, multiplicity, crisis, and synchronicity in higher order neural networks. Physical Review A, vol. 44(4), pp. R2259 - R2262.
dc.identifier.uri http://hdl.handle.net/10220/8128
dc.description.abstract We study a randomly diluted higher-order network of spinlike neurons that interact via Hebbian-type connections and derive and solve exact dynamical equations for a general block-sequential updating algorithm. The system has a variety of static and oscillatory solutions. The bifurcation parameters in the present model include neuronal interaction coefficients, the synchronicity parameter, and a rescaled noise level, which represents the combined effects of the random synaptic dilution, interference between stored patterns, and additional background noise.
dc.format.extent 4 p.
dc.language.iso en
dc.relation.ispartofseries Physical review A
dc.rights © 1991 The American Physical Society. This paper was published in Physical Review A and is made available as an electronic reprint (preprint) with permission of American Physical Society. The paper can be found at the following official URL: [http://dx.doi.org/10.1103/PhysRevA.44.R2259].  One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.
dc.subject DRNTU::Engineering::Electrical and electronic engineering.
dc.title Chaos, multiplicity, crisis, and synchronicity in higher order neural networks.
dc.type Journal Article
dc.contributor.school School of Electrical and Electronic Engineering
dc.identifier.doi http://dx.doi.org/10.1103/PhysRevA.44.R2259
dc.description.version Published version

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