Effective detection of coupling in short and noisy bivariate data

被引:40
作者
Bhattacharya, J [1 ]
Pereda, E
Petsche, H
机构
[1] CALTECH, Div Biol, Pasadena, CA 91125 USA
[2] Austrian Acad Sci, Commiss Sci Visualizat, A-1220 Vienna, Austria
[3] ITER, Dept Syst Engn, Tenerife, Spain
[4] Univ Vienna, Brain Res Inst, A-1010 Vienna, Austria
来源
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS | 2003年 / 33卷 / 01期
关键词
asymmetry; interdependency; noise; nonlinearity; state space; time series;
D O I
10.1109/TSMCB.2003.808175
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In the study of complex systems, one of the primary concerns is the characterization and quantification of interdependencies between different subsystems. In real-life systems, the nature of dependencies or coupling can be nonlinear and asymmetric, rendering the classical linear methods unsuitable for this purpose. Furthermore, experimental signals are noisy and short, which pose additional constraints for the measurement of underlying coupling. We discuss an index based on nonlinear dynamical system theory to measure the degree of coupling which can be asymmetric. The usefulness of this index has been demonstrated by several examples including simulated and real-life signals. This index is found to effectively disclose the nature and the degree of interactions even when the coupling is very weak and data are noisy and of limited length; by this way, new insight into the functioning of the underlying complex system is possible.
引用
收藏
页码:85 / 95
页数:11
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