Measuring synchronization in coupled model systems: A comparison of different approaches

被引:159
作者
Kreuz, Thomas
Mormann, Florian
Andrzejak, Ralph G.
Kraskov, Alexander
Lehnertz, Klaus
Grassberger, Peter
机构
[1] CNR, Ist Sistemi Complessi, I-50019 Florence, Italy
[2] Res Ctr Julich, John Neumann Inst Comp, Julich, Germany
[3] Univ Bonn, Dept Epileptol, D-5300 Bonn, Germany
[4] CALTECH, Div Biol, Pasadena, CA 91125 USA
[5] Univ Pompeu Fabra, Dept Tecnol, Barcelona, Spain
[6] Univ Bonn, Helmholtz Inst Radiat & Nucl Phys, D-5300 Bonn, Germany
关键词
nonlinear time series analysis; synchronization; coupled model systems;
D O I
10.1016/j.physd.2006.09.039
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
The investigation of synchronization phenomena on measured experimental data such as biological time series has recently become an increasing focus of interest. Different approaches for measuring synchronization have been proposed that rely on certain characteristic features of the dynamical system under investigation. For experimental data the underlying dynamics are usually not completely known, therefore it is difficult to decide a priori which synchronization measure is most suitable for an analysis. In this study we use three different coupled model systems to create a 'controlled' setting for a comparison of six different measures of synchronization. All measures are compared to each other with respect to their ability to distinguish between different levels of coupling and their robustness against noise. Results show that the measure to be applied to a certain task can not be chosen according to a fixed criterion but rather pragmatically as the measure which most reliably yields plausible information in test applications, although certain dynamical features of a system under investigation (e.g., power spectra, dimension) may render certain measures more suitable than others. (c) 2006 Elsevier B.V. All rights reserved.
引用
收藏
页码:29 / 42
页数:14
相关论文
共 58 条
[41]   Event synchronization: A simple and fast method to measure synchronicity and time delay patterns [J].
Quiroga, RQ ;
Kreuz, T ;
Grassberger, P .
PHYSICAL REVIEW E, 2002, 66 (04) :9-041904
[42]   Performance of different synchronization measures in real data: A case study on electroencephalographic signals [J].
Quiroga, RQ ;
Kraskov, A ;
Kreuz, T ;
Grassberger, P .
PHYSICAL REVIEW E, 2002, 65 (04) :14
[43]   Phase synchronization of chaotic oscillators [J].
Rosenblum, MG ;
Pikovsky, AS ;
Kurths, J .
PHYSICAL REVIEW LETTERS, 1996, 76 (11) :1804-1807
[44]   Comment on "Phase synchronization in discrete chaotic systems" [J].
Rosenblum, MG ;
Pikovsky, AS ;
Kurths, J .
PHYSICAL REVIEW E, 2001, 63 (05) :1-58201
[45]  
ROSENBLUM MG, 2001, HDB BIOL PHYS, P297
[46]   Data-driven optimal filtering for phase and frequency of noisy oscillations:: Application to vortex flow metering -: art. no. 016216 [J].
Rossberg, AG ;
Bartholomé, K ;
Timmer, J .
PHYSICAL REVIEW E, 2004, 69 (01) :11
[47]   GENERALIZED SYNCHRONIZATION OF CHAOS IN DIRECTIONALLY COUPLED CHAOTIC SYSTEMS [J].
RULKOV, NF ;
SUSHCHIK, MM ;
TSIMRING, LS ;
ABARBANEL, HDI .
PHYSICAL REVIEW E, 1995, 51 (02) :980-994
[48]   Detecting dynamical interdependence and generalized synchrony through mutual prediction in a neural ensemble [J].
Schiff, SJ ;
So, P ;
Chang, T ;
Burke, RE ;
Sauer, T .
PHYSICAL REVIEW E, 1996, 54 (06) :6708-6724
[49]   Measuring information transfer [J].
Schreiber, T .
PHYSICAL REVIEW LETTERS, 2000, 85 (02) :461-464
[50]   Bias analysis in entropy estimation [J].
Schürmann, T .
JOURNAL OF PHYSICS A-MATHEMATICAL AND GENERAL, 2004, 37 (27) :L295-L301