Inference of the cross-frequency coupling in a multiplex Kuramoto model via recurrence quantification analysis approach
Nikita Frolov, Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Russia; Vladimir Maksimenko, Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Russia; Dibakar Ghosh, Physics and Applied Mathematics Unit, Indian Statistical Institute, India
Abstract
Non-statiory dynamics of the broad-band neuronal oscillations, or brain rhythms, could be modelled via a heterogenous ensembles of weakly coupled Kuramoto phase oscillators. In this paper, we model the interaction slow and fast brain rhythms to examine the properties of cross-frequency synchronisation (CFS) by means of a multiplex graph analysis. In the framework of the multilayer approach, one layer of the complex network is adjusted to the dynamics of slow oscillations (theta-band, 4-8 Hz), while the other represents fast oscillations (beta-band, 15-30 Hz), and the interlayer connections define local cross-frequency coupling. We quantify the degree of synchronization between the layers using a recurrence-based approach - a technique for non-stationary data analysis. Based on the numerical results, we establish that cross-frequency coupling is detected by the appearance of the recurrence points at the diagonal lines parallel to the main diagonal of the joint recurrence plot and spaced from it at a distance multiple of the characteristic period of slow oscillations.
Speaker
Nikita Frolov (n.frolov@innopolis.ru)
Innopolis University
Russia
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