Pattern recognition, chaos, and multiplicity in neural networks of excitable systems. | Zendy

Allen T. Hjelmfelt | Zendy; John Ross | Zendy

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Pattern recognition, chaos, and multiplicity in neural networks of excitable systems.

Author(s) -

Allen T. Hjelmfelt,

John Ross

Publication year - 1994

Publication title -

proceedings of the national academy of sciences

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 5.011

H-Index - 771

eISSN - 1091-6490

pISSN - 0027-8424

DOI - 10.1073/pnas.91.1.63

Subject(s) - hebbian theory , chaotic , artificial neural network , bursting , transient (computer programming) , computer science , neuroscience , computation , chaos (operating system) , biological system , statistical physics , artificial intelligence , physics , algorithm , biology , operating system , computer security

We study a neural network composed of excitable FitzHugh neurons that interact by diffusive type connections. Patterns of neural activity may be stored by a Hebbian rule. The stored patterns are recalled and given by the transient activity of the neurons after the network has been perturbed by related patterns and relaxes back to its steady state. Periodic perturbations of the network are repeated requests for computations and result in simple periodic, complex periodic, and chaotic responses and corresponding computational performances.

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