On bifurcations and chaos in random neural networks View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1994-09

AUTHORS

B. Doyon, B. Cessac, M. Quoy, M. Samuelides

ABSTRACT

Chaos in nervous system is a fascinating but controversial field of investigation. To approach the role of chaos in the real brain, we theoretically and numerically investigate the occurrence of chaos inartificial neural networks. Most of the time, recurrent networks (with feedbacks) are fully connected. This architecture being not biologically plausible, the occurrence of chaos is studied here for a randomly diluted architecture. By normalizing the variance of synaptic weights, we produce a bifurcation parameter, dependent on this variance and on the slope of the transfer function, that allows a sustained activity and the occurrence of chaos when reaching a critical value. Even for weak connectivity and small size, we find numerical results in accordance with the theoretical ones previously established for fully connected infinite sized networks. The route towards chaos is numerically checked to be a quasi-periodic one, whatever the type of the first bifurcation is. Our results suggest that such high-dimensional networks behave like low-dimensional dynamical systems. More... »

PAGES

215-225

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf00709492

DOI

http://dx.doi.org/10.1007/bf00709492

DIMENSIONS

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