Oscillating systems with cointegrated phase processes

Research output: Contribution to journalJournal articlepeer-review

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Oscillating systems with cointegrated phase processes. / Østergaard, Jacob; Rahbek, Anders; Ditlevsen, Susanne.

In: Journal of Mathematical Biology, Vol. 75, No. 4, 2017, p. 845–883.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Østergaard, J, Rahbek, A & Ditlevsen, S 2017, 'Oscillating systems with cointegrated phase processes', Journal of Mathematical Biology, vol. 75, no. 4, pp. 845–883. https://doi.org/10.1007/s00285-017-1100-2

APA

Østergaard, J., Rahbek, A., & Ditlevsen, S. (2017). Oscillating systems with cointegrated phase processes. Journal of Mathematical Biology, 75(4), 845–883. https://doi.org/10.1007/s00285-017-1100-2

Vancouver

Østergaard J, Rahbek A, Ditlevsen S. Oscillating systems with cointegrated phase processes. Journal of Mathematical Biology. 2017;75(4):845–883. https://doi.org/10.1007/s00285-017-1100-2

Author

Østergaard, Jacob ; Rahbek, Anders ; Ditlevsen, Susanne. / Oscillating systems with cointegrated phase processes. In: Journal of Mathematical Biology. 2017 ; Vol. 75, No. 4. pp. 845–883.

Bibtex

@article{d3e64bf2a8e44e9a8b534e12fc30e566,
title = "Oscillating systems with cointegrated phase processes",
abstract = "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.",
keywords = "Cointegration, Coupled oscillators, EEG signals, Interacting dynamical system, Phase process, Synchronization, Winfree oscillator",
author = "Jacob {\O}stergaard and Anders Rahbek and Susanne Ditlevsen",
year = "2017",
doi = "10.1007/s00285-017-1100-2",
language = "English",
volume = "75",
pages = "845–883",
journal = "Journal of Mathematical Biology",
issn = "0303-6812",
publisher = "Springer",
number = "4",

}

RIS

TY - JOUR

T1 - Oscillating systems with cointegrated phase processes

AU - Østergaard, Jacob

AU - Rahbek, Anders

AU - Ditlevsen, Susanne

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - Cointegration

KW - Coupled oscillators

KW - EEG signals

KW - Interacting dynamical system

KW - Phase process

KW - Synchronization

KW - Winfree oscillator

U2 - 10.1007/s00285-017-1100-2

DO - 10.1007/s00285-017-1100-2

M3 - Journal article

C2 - 28138760

AN - SCOPUS:85010924548

VL - 75

SP - 845

EP - 883

JO - Journal of Mathematical Biology

JF - Journal of Mathematical Biology

SN - 0303-6812

IS - 4

ER -

ID: 174490998