This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by Berry, H. Articles by Quoy, M. Search for Related Content Social Bookmarking                         What's this?

Structure and Dynamics of Random Recurrent Neural Networks

INRIA Futurs, France, hugues.berry{at}inria.fr

Mathias Quoy

ETIS UMR8051, France, quoy{at}ensea.fr

Contrary to Hopfield-like networks, random recurrent neural networks (RRNN), where the couplings are random, exhibit complex dynamics (limit cycles, chaos). It is possible to store information in these networks through Hebbian learning. Eventually, learning "destroys" the dynamics and leads to a fixed point attractor. We investigate here the structural changes occurring in the network through learning. We show that a simple Hebbian learning rule organizes synaptic weight redistribution on the network from an initial homogeneous and random distribution to a heterogeneous one, where strong synaptic weights preferentially assemble in triangles. Hence learning organizes the network of the large synaptic weights as a "small-world" one

Key Words: associative memory • complex networks • Hebbian learning • chaotic neural networks

Adaptive Behavior, Vol. 14, No. 2, 129-137 (2006)
DOI: 10.1177/105971230601400204


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?

This article has been cited by other articles:

				M. Negrello and F. Pasemann Attractor Landscapes and Active Tracking: The Neurodynamics of Embodied Action Adaptive Behavior, April 1, 2008; 16(2-3): 196 - 216. [Abstract] [PDF]