Literature DB >> 18238084

Grouping synchronization in a pulse-coupled network of chaotic spiking oscillators.

H Nakano1, T Saito.   

Abstract

This paper studies a pulse-coupled network consisting of simple chaotic spiking oscillators (CSOs). If a unit oscillator and its neighbor(s) have (almost) the same parameter values, they exhibit in-phase synchronization of chaos. As the parameter values differ, they exhibit asynchronous phenomena. Based on such behavior, some synchronous groups appear partially in the network. Typical phenomena are verified in the laboratory via a simple test circuit. These phenomena can be evaluated numerically by using an effective mapping procedure. We then apply the proposed network to image segmentation. Using a lattice pulse-coupled network via grouping synchronous phenomena, the input image data can be segmented into some sub-regions.

Year:  2004        PMID: 18238084     DOI: 10.1109/TNN.2004.832807

Source DB:  PubMed          Journal:  IEEE Trans Neural Netw        ISSN: 1045-9227


  2 in total

1.  Nonlinear optimal control for the synchronization of biological neurons under time-delays.

Authors:  G Rigatos; P Wira; A Melkikh
Journal:  Cogn Neurodyn       Date:  2018-10-15       Impact factor: 5.082

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

Authors:  Nariman Mahdavi; Jürgen Kurths
Journal:  Cogn Neurodyn       Date:  2013-06-11       Impact factor: 5.082

  2 in total

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