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 t...

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Autores principales:	Patricia Wollstadt, Mario Martínez-Zarzuela, Raul Vicente, Francisco J Díaz-Pernas, Michael Wibral
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/be1a7bdd7a4a4f8599154998fc17283b
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spelling	oai:doaj.org-article:be1a7bdd7a4a4f8599154998fc17283b2021-11-25T06:06:57ZEfficient transfer entropy analysis of non-stationary neural time series.1932-620310.1371/journal.pone.0102833https://doaj.org/article/be1a7bdd7a4a4f8599154998fc17283b2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25068489/?tool=EBIhttps://doaj.org/toc/1932-6203Information 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.Patricia WollstadtMario Martínez-ZarzuelaRaul VicenteFrancisco J Díaz-PernasMichael WibralPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 7, p e102833 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Patricia Wollstadt Mario Martínez-Zarzuela Raul Vicente Francisco J Díaz-Pernas Michael Wibral Efficient transfer entropy analysis of non-stationary neural time series.
description	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.
format	article
author	Patricia Wollstadt Mario Martínez-Zarzuela Raul Vicente Francisco J Díaz-Pernas Michael Wibral
author_facet	Patricia Wollstadt Mario Martínez-Zarzuela Raul Vicente Francisco J Díaz-Pernas Michael Wibral
author_sort	Patricia Wollstadt
title	Efficient transfer entropy analysis of non-stationary neural time series.
title_short	Efficient transfer entropy analysis of non-stationary neural time series.
title_full	Efficient transfer entropy analysis of non-stationary neural time series.
title_fullStr	Efficient transfer entropy analysis of non-stationary neural time series.
title_full_unstemmed	Efficient transfer entropy analysis of non-stationary neural time series.
title_sort	efficient transfer entropy analysis of non-stationary neural time series.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/be1a7bdd7a4a4f8599154998fc17283b
work_keys_str_mv	AT patriciawollstadt efficienttransferentropyanalysisofnonstationaryneuraltimeseries AT mariomartinezzarzuela efficienttransferentropyanalysisofnonstationaryneuraltimeseries AT raulvicente efficienttransferentropyanalysisofnonstationaryneuraltimeseries AT franciscojdiazpernas efficienttransferentropyanalysisofnonstationaryneuraltimeseries AT michaelwibral efficienttransferentropyanalysisofnonstationaryneuraltimeseries
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Efficient transfer entropy analysis of non-stationary neural time series.

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