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Dynamical inference: where phase synchronization and generalized synchronization meet

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Dynamical inference: where phase synchronization and generalized synchronization meet. / Stankovski, Tomislav; McClintock, Peter V. E. ; Stefanovska, Aneta.
In: Physical Review E, Vol. 89, 062909, 10.06.2014, p. 1-11.

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@article{c41cbe8f35544fbb8b0ffe85f86662cc,
title = "Dynamical inference: where phase synchronization and generalized synchronization meet",
abstract = "Synchronization is a widespread phenomenon that occurs among interacting oscillatory systems. It facilitates their temporal coordination and can lead to the emergence of spontaneous order. The detection of synchronization from the time series of such systems is of great importance for the understanding and prediction of their dynamics, and several methods for doing so have been introduced. However, the common case where the interacting systems have time-variable characteristic frequencies and coupling parameters, and may also be subject to continuous external perturbation and noise, still presents a major challenge. Here we apply recent developments in dynamical Bayesian inference to tackle these problems. In particular, we discuss how to detect phase slips and the existence of deterministic coupling from measured data, and we unify the concepts of phase synchronization and general synchronization. Starting from phase or state observables, we present methods for the detection of both phase and generalized synchronization. The consistency and equivalence of phase and generalized synchronization are further demonstrated, by the analysis of time series from analog electronic simulations of coupled nonautonomous van der Pol oscillators. We demonstrate that the detection methods work equally well on numerically simulated chaotic systems. In all the cases considered, we show that dynamical Bayesian inference can clearly identify noise-induced phase slips and distinguish coherence from intrinsic coupling-induced synchronization.",
author = "Tomislav Stankovski and McClintock, {Peter V. E.} and Aneta Stefanovska",
year = "2014",
month = jun,
day = "10",
doi = "10.1103/PhysRevE.89.062909",
language = "English",
volume = "89",
pages = "1--11",
journal = "Physical Review E",
issn = "1539-3755",
publisher = "American Physical Society",

}

RIS

TY - JOUR

T1 - Dynamical inference

T2 - where phase synchronization and generalized synchronization meet

AU - Stankovski, Tomislav

AU - McClintock, Peter V. E.

AU - Stefanovska, Aneta

PY - 2014/6/10

Y1 - 2014/6/10

N2 - Synchronization is a widespread phenomenon that occurs among interacting oscillatory systems. It facilitates their temporal coordination and can lead to the emergence of spontaneous order. The detection of synchronization from the time series of such systems is of great importance for the understanding and prediction of their dynamics, and several methods for doing so have been introduced. However, the common case where the interacting systems have time-variable characteristic frequencies and coupling parameters, and may also be subject to continuous external perturbation and noise, still presents a major challenge. Here we apply recent developments in dynamical Bayesian inference to tackle these problems. In particular, we discuss how to detect phase slips and the existence of deterministic coupling from measured data, and we unify the concepts of phase synchronization and general synchronization. Starting from phase or state observables, we present methods for the detection of both phase and generalized synchronization. The consistency and equivalence of phase and generalized synchronization are further demonstrated, by the analysis of time series from analog electronic simulations of coupled nonautonomous van der Pol oscillators. We demonstrate that the detection methods work equally well on numerically simulated chaotic systems. In all the cases considered, we show that dynamical Bayesian inference can clearly identify noise-induced phase slips and distinguish coherence from intrinsic coupling-induced synchronization.

AB - Synchronization is a widespread phenomenon that occurs among interacting oscillatory systems. It facilitates their temporal coordination and can lead to the emergence of spontaneous order. The detection of synchronization from the time series of such systems is of great importance for the understanding and prediction of their dynamics, and several methods for doing so have been introduced. However, the common case where the interacting systems have time-variable characteristic frequencies and coupling parameters, and may also be subject to continuous external perturbation and noise, still presents a major challenge. Here we apply recent developments in dynamical Bayesian inference to tackle these problems. In particular, we discuss how to detect phase slips and the existence of deterministic coupling from measured data, and we unify the concepts of phase synchronization and general synchronization. Starting from phase or state observables, we present methods for the detection of both phase and generalized synchronization. The consistency and equivalence of phase and generalized synchronization are further demonstrated, by the analysis of time series from analog electronic simulations of coupled nonautonomous van der Pol oscillators. We demonstrate that the detection methods work equally well on numerically simulated chaotic systems. In all the cases considered, we show that dynamical Bayesian inference can clearly identify noise-induced phase slips and distinguish coherence from intrinsic coupling-induced synchronization.

U2 - 10.1103/PhysRevE.89.062909

DO - 10.1103/PhysRevE.89.062909

M3 - Journal article

VL - 89

SP - 1

EP - 11

JO - Physical Review E

JF - Physical Review E

SN - 1539-3755

M1 - 062909

ER -