Next Article in Journal
Translation of Ludwig Boltzmann’s Paper “On the Relationship between the Second Fundamental Theorem of the Mechanical Theory of Heat and Probability Calculations Regarding the Conditions for Thermal Equilibrium” Sitzungberichte der Kaiserlichen Akademie der Wissenschaften. Mathematisch-Naturwissen Classe. Abt. II, LXXVI 1877, pp 373-435 (Wien. Ber. 1877, 76:373-435). Reprinted in Wiss. Abhandlungen, Vol. II, reprint 42, p. 164-223, Barth, Leipzig, 1909
Next Article in Special Issue
Contribution to Transfer Entropy Estimation via the k-Nearest-Neighbors Approach
Previous Article in Journal
A Simple Decoder for Topological Codes
Previous Article in Special Issue
A Recipe for the Estimation of Information Flow in a Dynamical System

Assessing Coupling Dynamics from an Ensemble of Time Series

Netherlands Institute for Neuroscience, Meibergdreef 47, Amsterdam 1105 BA, The Netherlands
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
Department of Mathematics, Tampere University of Technology, Korkeakoulunkatu 10, Tampere FI-33720, Finland
Instituto de Fisica Interdisciplinar y Sistemas Complejos (CSIC-UIB), Campus Universitat de les Illes Balears E-07122 Palma de Mallorca, Spain
Institut für Kognitionswissenschaft, University of Osnabrück, Albrechtstrasse 28, Osnabrück 49076, Germany
Institute of Computer Science, University of Tartu, J. Liivi 2, Tartu 50409, Estonia
Author to whom correspondence should be addressed.
Academic Editor: Deniz Gencaga
Entropy 2015, 17(4), 1958-1970;
Received: 30 November 2014 / Revised: 28 February 2015 / Accepted: 19 March 2015 / Published: 2 April 2015
(This article belongs to the Special Issue Transfer Entropy)
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. View Full-Text
Keywords: entropy; transfer entropy; estimator; ensemble; trial; time series entropy; transfer entropy; estimator; ensemble; trial; time series
Show Figures

MDPI and ACS Style

Gómez-Herrero, G.; Wu, W.; Rutanen, K.; Soriano, M.C.; Pipa, G.; Vicente, R. Assessing Coupling Dynamics from an Ensemble of Time Series. Entropy 2015, 17, 1958-1970.

AMA Style

Gómez-Herrero G, Wu W, Rutanen K, Soriano MC, Pipa G, Vicente R. Assessing Coupling Dynamics from an Ensemble of Time Series. Entropy. 2015; 17(4):1958-1970.

Chicago/Turabian Style

Gómez-Herrero, Germán, Wei Wu, Kalle Rutanen, Miguel C. Soriano, Gordon Pipa, and Raul Vicente. 2015. "Assessing Coupling Dynamics from an Ensemble of Time Series" Entropy 17, no. 4: 1958-1970.

Find Other Styles

Article Access Map by Country/Region

Only visits after 24 November 2015 are recorded.
Back to TopTop