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Inferring connectivity of an oscillatory network via the phase dynamics reconstruction

Authors :
Michael Rosenblum
Arkady Pikovsky
Source :
Frontiers in Network Physiology, Vol 3 (2023)
Publication Year :
2023
Publisher :
Frontiers Media S.A., 2023.

Abstract

We review an approach for reconstructing oscillatory networks’ undirected and directed connectivity from data. The technique relies on inferring the phase dynamics model. The central assumption is that we observe the outputs of all network nodes. We distinguish between two cases. In the first one, the observed signals represent smooth oscillations, while in the second one, the data are pulse-like and can be viewed as point processes. For the first case, we discuss estimating the true phase from a scalar signal, exploiting the protophase-to-phase transformation. With the phases at hand, pairwise and triplet synchronization indices can characterize the undirected connectivity. Next, we demonstrate how to infer the general form of the coupling functions for two or three oscillators and how to use these functions to quantify the directional links. We proceed with a different treatment of networks with more than three nodes. We discuss the difference between the structural and effective phase connectivity that emerges due to high-order terms in the coupling functions. For the second case of point-process data, we use the instants of spikes to infer the phase dynamics model in the Winfree form directly. This way, we obtain the network’s coupling matrix in the first approximation in the coupling strength.

Details

Language :
English
ISSN :
26740109
Volume :
3
Database :
Directory of Open Access Journals
Journal :
Frontiers in Network Physiology
Publication Type :
Academic Journal
Accession number :
edsdoj.188170c19d41c5a94ceb16c7957928
Document Type :
article
Full Text :
https://doi.org/10.3389/fnetp.2023.1298228