Literature DB >> 16803103

Determination of a coupling function in multicoupled oscillators.

Jun Miyazaki1, Shuichi Kinoshita.   

Abstract

A new method to determine a coupling function in a phase model is theoretically derived for coupled self-sustained oscillators and applied to Belousov-Zhabotinsky (BZ) oscillators. The synchronous behavior of two coupled BZ reactors is explained extremely well in terms of the coupling function thus obtained. This method is expected to be applicable to weakly coupled multioscillator systems, in which mutual coupling among nearly identical oscillators occurs in a similar manner. The importance of higher-order harmonic terms involved in the coupling function is also discussed.

Entities:  

Year:  2006        PMID: 16803103     DOI: 10.1103/PhysRevLett.96.194101

Source DB:  PubMed          Journal:  Phys Rev Lett        ISSN: 0031-9007            Impact factor:   9.161


  10 in total

1.  Engineering the synchronization of neuron action potentials using global time-delayed feedback stimulation.

Authors:  Craig G Rusin; Sarah E Johnson; Jaideep Kapur; John L Hudson
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2011-12-06

2.  A practical method for estimating coupling functions in complex dynamical systems.

Authors:  Isao T Tokuda; Zoran Levnajic; Kazuyoshi Ishimura
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2019-10-28       Impact factor: 4.226

3.  Anti-phase collective synchronization with intrinsic in-phase coupling of two groups of electrochemical oscillators.

Authors:  Michael Sebek; Yoji Kawamura; Ashley M Nott; István Z Kiss
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2019-10-28       Impact factor: 4.226

4.  A Framework for Engineering the Collective Behavior of Complex Rhythmic Systems.

Authors:  Craig G Rusin; István Z Kiss; Hiroshi Kori; John L Hudson
Journal:  Ind Eng Chem Res       Date:  2009-03-16       Impact factor: 3.720

5.  Neural Cross-Frequency Coupling Functions.

Authors:  Tomislav Stankovski; Valentina Ticcinelli; Peter V E McClintock; Aneta Stefanovska
Journal:  Front Syst Neurosci       Date:  2017-06-15

6.  Using Bayesian dynamical systems, model averaging and neural networks to determine interactions between socio-economic indicators.

Authors:  Björn R H Blomqvist; Richard P Mann; David J T Sumpter
Journal:  PLoS One       Date:  2018-05-09       Impact factor: 3.240

7.  Spatio-temporal dynamics in collective frog choruses examined by mathematical modeling and field observations.

Authors:  Ikkyu Aihara; Takeshi Mizumoto; Takuma Otsuka; Hiromitsu Awano; Kohei Nagira; Hiroshi G Okuno; Kazuyuki Aihara
Journal:  Sci Rep       Date:  2014-01-27       Impact factor: 4.379

8.  Interaction mechanisms quantified from dynamical features of frog choruses.

Authors:  Kaiichiro Ota; Ikkyu Aihara; Toshio Aoyagi
Journal:  R Soc Open Sci       Date:  2020-03-18       Impact factor: 2.963

9.  Coupling functions: dynamical interaction mechanisms in the physical, biological and social sciences.

Authors:  Tomislav Stankovski; Tiago Pereira; Peter V E McClintock; Aneta Stefanovska
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2019-10-28       Impact factor: 4.226

10.  Noninvasive inference methods for interaction and noise intensities of coupled oscillators using only spike time data.

Authors:  Fumito Mori; Hiroshi Kori
Journal:  Proc Natl Acad Sci U S A       Date:  2022-02-08       Impact factor: 12.779

  10 in total

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