Discrete-time dynamics of coupled quasi-periodic and chaotic neural network oscillators

Author

Wang, Xin

Author_Institution

Dept. of Math., Univ., of Southern California, Los Angeles, CA, USA

Volume

3

fYear

1992

fDate

7-11 Jun 1992

Firstpage

517

Abstract

An analytical and computational study of the collective behavior of coupled discrete-time neural network oscillators is presented, with special emphasis on the oscillators being quasi-periodic and chaotic through the Hopf bifurcation and period-doubling bifurcations as the neuron gain is varied. The effects of various coupling structures on the dynamic features like frequency locking, amplitude death, and spatiotemporal chaos, which are interesting to neural information processing such as stimulus-dependent synchronization and pattern formation, are investigated

Keywords

chaos; dynamics; neural nets; oscillators; Hopf bifurcation; amplitude death; chaotic neural network oscillators; coupled discrete-time neural network oscillators; discrete-time dynamics; frequency locking; neural information processing; neuron gain; pattern formation; period-doubling bifurcations; spatiotemporal chaos; stimulus-dependent synchronization; Bifurcation; Chaos; Computer networks; Frequency synchronization; Information processing; Neural networks; Neurons; Oscillators; Pattern formation; Spatiotemporal phenomena;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1992. IJCNN., International Joint Conference on

Conference_Location

Baltimore, MD

Print_ISBN

0-7803-0559-0

Type

conf

DOI

10.1109/IJCNN.1992.227122

Filename

227122