HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Bertschinger, N. Articles by Natschläger, T. Search for Related Content PubMed PubMed Citation Articles by Bertschinger, N. Articles by Natschläger, T.

Real-Time Computation at the Edge of Chaos in Recurrent Neural Networks

nilsb{at}igi.tugraz.at, Institute for Theoretical Computer Science, Technische Universitaet Graz, A-8010 Graz, Austria

Thomas Natschläger

Thomas.Natschlaeger{at}scch.at, Software Compentence Center Hagenberg, A-4232 Hagenberg, Austria

Depending on the connectivity, recurrent networks of simple computational units can show very different types of dynamics, ranging from totally ordered to chaotic. We analyze how the type of dynamics (ordered or chaotic) exhibited by randomly connected networks of threshold gates driven by a time-varying input signal depends on the parameters describing the distribution of the connectivity matrix. In particular, we calculate the critical boundary in parameter space where the transition from ordered to chaotic dynamics takes place. Employing a recently developed framework for analyzing real-time computations, we show that only near the critical boundary can such networks perform complex computations on time series. Hence, this result strongly supports conjectures that dynamical systems that are capable of doing complex computational tasks should operate near the edge of chaos, that is, the transition from ordered to chaotic dynamics.

This article has been cited by other articles:

				T. Nowotny, A. Szucs, R. Levi, and A. I. Selverston Models wagging the dog: are circuits constructed with disparate parameters? Neural Comput., August 1, 2007; 19(8): 1985 - 2003. [Abstract] [Full Text] [PDF]

				J. Triesch Synergies between intrinsic and synaptic plasticity mechanisms. Neural Comput., April 1, 2007; 19(4): 885 - 909. [Abstract] [Full Text] [PDF]

				D. Durstewitz and T. Gabriel Dynamical Basis of Irregular Spiking in NMDA-Driven Prefrontal Cortex Neurons Cereb Cortex, April 1, 2007; 17(4): 894 - 908. [Abstract] [Full Text] [PDF]

				M. C. Ozturk, D. Xu, and J. C. Principe Analysis and Design of Echo State Networks Neural Comput., January 1, 2006; 19(1): 111 - 138. [Abstract] [Full Text] [PDF]