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A mathematical analysis of the effects of Hebbian learning rules on the dynamics and structure of discrete-time random recurrent neural networks.

Benoit Siri 1 Hugues Berry 2, 1 Bruno Cessac 3, 4, 5 Bruno Delord 6 Mathias Quoy 7 
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, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
4 ODYSSEE - Computer and biological vision
DI-ENS - Département d'informatique - ENS Paris, CRISAM - Inria Sophia Antipolis - Méditerranée , ENS-PSL - École normale supérieure - Paris, Inria Paris-Rocquencourt, ENPC - École des Ponts ParisTech
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https://hal.inria.fr/inria-00633582
Contributor : Hugues Berry Connect in order to contact the contributor
Submitted on : Tuesday, October 18, 2011 - 9:37:50 PM
Last modification on : Friday, August 5, 2022 - 2:46:00 PM

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  • HAL Id : inria-00633582, version 1

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Benoit Siri, Hugues Berry, Bruno Cessac, Bruno Delord, Mathias Quoy. A mathematical analysis of the effects of Hebbian learning rules on the dynamics and structure of discrete-time random recurrent neural networks.. Neural Computation, 2008, 20, pp.2937-2966. ⟨inria-00633582⟩

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