Oscillating systems with cointegrated phase processes
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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 journal › Journal article › Research › peer-review
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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