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Oscillating systems with cointegrated phase processes

We present cointegration analysis as a method to infer the network structure of a linearly phase coupled oscillating system. By defining a class of oscillating systems with interacting phases, we derive a data generating process where we can specify the coupling structure of a network that resembles...

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Detalles Bibliográficos
Autores principales: Østergaard, Jacob, Rahbek, Anders, Ditlevsen, Susanne
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5562904/
https://www.ncbi.nlm.nih.gov/pubmed/28138760
http://dx.doi.org/10.1007/s00285-017-1100-2
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author Østergaard, Jacob
Rahbek, Anders
Ditlevsen, Susanne
author_facet Østergaard, Jacob
Rahbek, Anders
Ditlevsen, Susanne
author_sort Østergaard, Jacob
collection PubMed
description We present cointegration analysis as a method to infer the network structure of a linearly phase coupled oscillating system. By defining a class of oscillating systems with interacting phases, we derive a data generating process where we can specify the coupling structure of a network that resembles biological processes. In particular we study a network of Winfree oscillators, for which we present a statistical analysis of various simulated networks, where we conclude on the coupling structure: the direction of feedback in the phase processes and proportional coupling strength between individual components of the system. We show that we can correctly classify the network structure for such a system by cointegration analysis, for various types of coupling, including uni-/bi-directional and all-to-all coupling. Finally, we analyze a set of EEG recordings and discuss the current applicability of cointegration analysis in the field of neuroscience.
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spelling pubmed-55629042017-09-01 Oscillating systems with cointegrated phase processes Østergaard, Jacob Rahbek, Anders Ditlevsen, Susanne J Math Biol Article We present cointegration analysis as a method to infer the network structure of a linearly phase coupled oscillating system. By defining a class of oscillating systems with interacting phases, we derive a data generating process where we can specify the coupling structure of a network that resembles biological processes. In particular we study a network of Winfree oscillators, for which we present a statistical analysis of various simulated networks, where we conclude on the coupling structure: the direction of feedback in the phase processes and proportional coupling strength between individual components of the system. We show that we can correctly classify the network structure for such a system by cointegration analysis, for various types of coupling, including uni-/bi-directional and all-to-all coupling. Finally, we analyze a set of EEG recordings and discuss the current applicability of cointegration analysis in the field of neuroscience. Springer Berlin Heidelberg 2017-01-30 2017 /pmc/articles/PMC5562904/ /pubmed/28138760 http://dx.doi.org/10.1007/s00285-017-1100-2 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Article
Østergaard, Jacob
Rahbek, Anders
Ditlevsen, Susanne
Oscillating systems with cointegrated phase processes
title Oscillating systems with cointegrated phase processes
title_full Oscillating systems with cointegrated phase processes
title_fullStr Oscillating systems with cointegrated phase processes
title_full_unstemmed Oscillating systems with cointegrated phase processes
title_short Oscillating systems with cointegrated phase processes
title_sort oscillating systems with cointegrated phase processes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5562904/
https://www.ncbi.nlm.nih.gov/pubmed/28138760
http://dx.doi.org/10.1007/s00285-017-1100-2
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