Efficient transfer entropy analysis of non-stationary neural time series

  • Information theory allows us to investigate information processing in neural systems in terms of information transfer, storage and modification. Especially the measure of information transfer, transfer entropy, has seen a dramatic surge of interest in neuroscience. Estimating transfer entropy from two processes requires the observation of multiple realizations of these processes to estimate associated probability density functions. To obtain these necessary observations, available estimators typically assume stationarity of processes to allow pooling of observations over time. This assumption however, is a major obstacle to the application of these estimators in neuroscience as observed processes are often non-stationary. As a solution, Gomez-Herrero and colleagues theoretically showed that the stationarity assumption may be avoided by estimating transfer entropy from an ensemble of realizations. Such an ensemble of realizations is often readily available in neuroscience experiments in the form of experimental trials. Thus, in this work we combine the ensemble method with a recently proposed transfer entropy estimator to make transfer entropy estimation applicable to non-stationary time series. We present an efficient implementation of the approach that is suitable for the increased computational demand of the ensemble method's practical application. In particular, we use a massively parallel implementation for a graphics processing unit to handle the computationally most heavy aspects of the ensemble method for transfer entropy estimation. We test the performance and robustness of our implementation on data from numerical simulations of stochastic processes. We also demonstrate the applicability of the ensemble method to magnetoencephalographic data. While we mainly evaluate the proposed method for neuroscience data, we expect it to be applicable in a variety of fields that are concerned with the analysis of information transfer in complex biological, social, and artificial systems.

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Author:Patricia Wollstadt, Mario Martínez-Zarzuela, Raul Vicente, Francisco J. Díaz-Pernas, Michael WibralORCiDGND
URN:urn:nbn:de:hebis:30:3-347113
DOI:https://doi.org/10.1371/journal.pone.0102833
ISSN:1932-6203
Parent Title (English):PLoS One
Publisher:PLoS
Place of publication:Lawrence, Kan.
Document Type:Article
Language:English
Date of Publication (online):2014/07/28
Date of first Publication:2014/07/28
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2014/08/05
Volume:9
Issue:(7):e102833
Page Number:22
Note:
Copyright: © 2014 Wollstadt et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
HeBIS-PPN:366010034
Institutes:Medizin / Medizin
Wissenschaftliche Zentren und koordinierte Programme / Frankfurt Institute for Advanced Studies (FIAS)
Angeschlossene und kooperierende Institutionen / MPI für Hirnforschung
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung 4.0