Skip to Main content Skip to Navigation
Journal articles

Structure and dynamics of random recurrent neural networks

Hugues Berry 1 Mathias Quoy 2
1 ALCHEMY - Architectures, Languages and Compilers to Harness the End of Moore Years
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France
Abstract : In contradiction with 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 netwoks through hebbian learning. Eventually, learning ``destroys'' the dynamics and leads to a fixed point attractor. We investigate here the structural change in the networks through learning, and show a ``small-world'' effect.
Complete list of metadata

Cited literature [23 references]  Display  Hide  Download

https://hal.inria.fr/inria-00001065
Contributor : Hugues Berry <>
Submitted on : Sunday, January 29, 2006 - 7:46:58 PM
Last modification on : Monday, January 25, 2021 - 3:16:03 PM
Long-term archiving on: : Saturday, April 3, 2010 - 9:57:24 PM

Identifiers

  • HAL Id : inria-00001065, version 1

Collections

Citation

Hugues Berry, Mathias Quoy. Structure and dynamics of random recurrent neural networks. Adaptive Behavior, SAGE Publications, 2006, pp.129-137. ⟨inria-00001065⟩

Share

Metrics

Record views

506

Files downloads

646