Synchrony based learning rule of Hopfield like chaotic neural networks with desirable structure

Nariman Mahdavi, Jurgen Kurths

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

In this paper a new learning rule for the coupling weights tuning of Hopfield like chaotic neural networks is developed in such a way that all neurons behave in a synchronous manner, while the desirable structure of the network is preserved during the learning process. The proposed learning rule is based on sufficient synchronization criteria, on the eigenvalues of the weight matrix belonging to the neural network and the idea of Structured Inverse Eigenvalue Problem. Our developed learning rule not only synchronizes all neuron’s outputs with each other in a desirable topology, but also enables us to enhance the synchronizability of the networks by choosing the appropriate set of weight matrix eigenvalues. Specifically, this method is evaluated by performing simulations on the scale-free topology.
Original languageEnglish
Pages (from-to)151-156
Number of pages6
JournalCognitive Neurodynamics
Volume8
Issue number2
Early online date11 Jun 2013
DOIs
Publication statusPublished - Apr 2014

Keywords

  • synchrony based learning
  • chaotic neural networks
  • structure inverse eigenvalue problem
  • scale-free networks

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