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

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

Content Provider	Springer Nature Link
Author	Mahdavi, Nariman Kurths, Jürgen
Copyright Year	2013
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.
Starting Page	151
Ending Page	156
Page Count	6
File Format	PDF
ISSN	18714080
Journal	Cognitive Neurodynamics
Volume Number	8
Issue Number	2
e-ISSN	18714099
Language	English
Publisher	Springer Netherlands
Publisher Date	2013-06-11
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Synchrony based learning Chaotic neural networks Structure inverse eigenvalue problem Scale-free networks Biomedicine general Neurosciences Computer Science Artificial Intelligence (incl. Robotics) Biochemistry Cognitive Psychology
Content Type	Text
Resource Type	Article
Subject	Cognitive Neuroscience

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