Assessing Coupling Dynamics from an Ensemble of Time Series

Entropy

Germán Gómez-Herrero⁵  Wei Wu³  Kalle Rutanen¹  Miguel C. Soriano⁶  Gordon Pipa²  Raul Vicente⁴ 

[1]Department of Mathematics, Tampere University of Technology, Korkeakoulunkatu 10, Tampere FI-33720, Finland

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[3]Institut für Kognitionswissenschaft, University of Osnabrück, Albrechtstrasse 28, Osnabrück 49076, Germany

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[5]Lab of Neurophysics and Neurophysiology, Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, 96 JinZhai Rd., Hefei 230026, China

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[7]Institute of Computer Science, University of Tartu, J. Liivi 2, Tartu 50409, Estonia

[8]Netherlands Institute for Neuroscience, Meibergdreef 47, Amsterdam 1105 BA, The Netherlands

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[10]Instituto de Fisica Interdisciplinar y Sistemas Complejos (CSIC-UIB), Campus Universitat de les Illes Balears E-07122 Palma de Mallorca, Spain

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关键词: entropy;    transfer entropy;    estimator;    ensemble;    trial;    time series;   

DOI  :  10.3390/e17041958

来源: mdpi

【 摘 要 】

Finding interdependency relations between time series provides valuable knowledge about the processes that generated the signals. Information theory sets a natural framework for important classes of statistical dependencies. However, a reliable estimation from information-theoretic functionals is hampered when the dependency to be assessed is brief or evolves in time. Here, we show that these limitations can be partly alleviated when we have access to an ensemble of independent repetitions of the time series. In particular, we gear a data-efficient estimator of probability densities to make use of the full structure of trial-based measures. By doing so, we can obtain time-resolved estimates for a family of entropy combinations (including mutual information, transfer entropy and their conditional counterparts), which are more accurate than the simple average of individual estimates over trials. We show with simulated and real data generated by coupled electronic circuits that the proposed approach allows one to recover the time-resolved dynamics of the coupling between different subsystems.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland