Measuring information-transfer delays.

In complex networks such as gene networks, traffic systems or brain circuits it is important to understand how long it takes for the different parts of the network to effectively influence one another. In the brain, for example, axonal delays between brain areas can amount to several tens of millise...

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Autores principales:	Michael Wibral, Nicolae Pampu, Viola Priesemann, Felix Siebenhühner, Hannes Seiwert, Michael Lindner, Joseph T Lizier, Raul Vicente
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1fe937c80d804a5ca3223d5df66e7db6
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spelling	oai:doaj.org-article:1fe937c80d804a5ca3223d5df66e7db62021-11-18T07:55:38ZMeasuring information-transfer delays.1932-620310.1371/journal.pone.0055809https://doaj.org/article/1fe937c80d804a5ca3223d5df66e7db62013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23468850/?tool=EBIhttps://doaj.org/toc/1932-6203In complex networks such as gene networks, traffic systems or brain circuits it is important to understand how long it takes for the different parts of the network to effectively influence one another. In the brain, for example, axonal delays between brain areas can amount to several tens of milliseconds, adding an intrinsic component to any timing-based processing of information. Inferring neural interaction delays is thus needed to interpret the information transfer revealed by any analysis of directed interactions across brain structures. However, a robust estimation of interaction delays from neural activity faces several challenges if modeling assumptions on interaction mechanisms are wrong or cannot be made. Here, we propose a robust estimator for neuronal interaction delays rooted in an information-theoretic framework, which allows a model-free exploration of interactions. In particular, we extend transfer entropy to account for delayed source-target interactions, while crucially retaining the conditioning on the embedded target state at the immediately previous time step. We prove that this particular extension is indeed guaranteed to identify interaction delays between two coupled systems and is the only relevant option in keeping with Wiener's principle of causality. We demonstrate the performance of our approach in detecting interaction delays on finite data by numerical simulations of stochastic and deterministic processes, as well as on local field potential recordings. We also show the ability of the extended transfer entropy to detect the presence of multiple delays, as well as feedback loops. While evaluated on neuroscience data, we expect the estimator to be useful in other fields dealing with network dynamics.Michael WibralNicolae PampuViola PriesemannFelix SiebenhühnerHannes SeiwertMichael LindnerJoseph T LizierRaul VicentePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 2, p e55809 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Michael Wibral Nicolae Pampu Viola Priesemann Felix Siebenhühner Hannes Seiwert Michael Lindner Joseph T Lizier Raul Vicente Measuring information-transfer delays.
description	In complex networks such as gene networks, traffic systems or brain circuits it is important to understand how long it takes for the different parts of the network to effectively influence one another. In the brain, for example, axonal delays between brain areas can amount to several tens of milliseconds, adding an intrinsic component to any timing-based processing of information. Inferring neural interaction delays is thus needed to interpret the information transfer revealed by any analysis of directed interactions across brain structures. However, a robust estimation of interaction delays from neural activity faces several challenges if modeling assumptions on interaction mechanisms are wrong or cannot be made. Here, we propose a robust estimator for neuronal interaction delays rooted in an information-theoretic framework, which allows a model-free exploration of interactions. In particular, we extend transfer entropy to account for delayed source-target interactions, while crucially retaining the conditioning on the embedded target state at the immediately previous time step. We prove that this particular extension is indeed guaranteed to identify interaction delays between two coupled systems and is the only relevant option in keeping with Wiener's principle of causality. We demonstrate the performance of our approach in detecting interaction delays on finite data by numerical simulations of stochastic and deterministic processes, as well as on local field potential recordings. We also show the ability of the extended transfer entropy to detect the presence of multiple delays, as well as feedback loops. While evaluated on neuroscience data, we expect the estimator to be useful in other fields dealing with network dynamics.
format	article
author	Michael Wibral Nicolae Pampu Viola Priesemann Felix Siebenhühner Hannes Seiwert Michael Lindner Joseph T Lizier Raul Vicente
author_facet	Michael Wibral Nicolae Pampu Viola Priesemann Felix Siebenhühner Hannes Seiwert Michael Lindner Joseph T Lizier Raul Vicente
author_sort	Michael Wibral
title	Measuring information-transfer delays.
title_short	Measuring information-transfer delays.
title_full	Measuring information-transfer delays.
title_fullStr	Measuring information-transfer delays.
title_full_unstemmed	Measuring information-transfer delays.
title_sort	measuring information-transfer delays.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/1fe937c80d804a5ca3223d5df66e7db6
work_keys_str_mv	AT michaelwibral measuringinformationtransferdelays AT nicolaepampu measuringinformationtransferdelays AT violapriesemann measuringinformationtransferdelays AT felixsiebenhuhner measuringinformationtransferdelays AT hannesseiwert measuringinformationtransferdelays AT michaellindner measuringinformationtransferdelays AT josephtlizier measuringinformationtransferdelays AT raulvicente measuringinformationtransferdelays
_version_	1718422717059825664

Measuring information-transfer delays.

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