Coupling functions:universal insights into dynamical interaction mechanisms

By Tomislav Stankovski, Tiago Pereira, Peter V. E. McClintock, Aneta Stefanovska. 2017

257 citations recorded by the source corpus.

The dynamical systems found in Nature are rarely isolated. Instead they interact and influence each other. The coupling functions that connect them contain detailed information about the functional mechanisms underlying the interactions and prescribe the physical rule specifying how an interaction occurs. Here, we aim to present a coherent and comprehensive review encompassing the rapid progress made recently in the analysis, understanding and applications of coupling functions. The basic concepts and characteristics of coupling functions are presented through demonstrative examples of different domains, revealing the mechanisms and emphasizing their multivariate nature. The theory of coupling functions is discussed through gradually increasing complexity from strong and weak interactions to globally-coupled systems and networks. A variety of methods that have been developed for the detection and reconstruction of coupling functions from measured data is described. These methods are based on different statistical techniques for dynamical inference. Stemming from physics, such methods are being applied in diverse areas of science and technology, including chemistry, biology, physiology, neuroscience, social sciences, mechanics and secure communications. This breadth of application illustrates the universality of coupling functions for studying the interaction mechanisms of coupled dynamical systems.

Subfields: Atomic and Molecular Physics, and Optics, Cognitive Neuroscience, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/70e6a18571779fb8e9d2fc86017807c417820968

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Citations in the corpus

What are higher-order networks? (2021, background): “Specifically, the collective network dynamics are determined by (i) the structure of the graph G, as encoded in the adjacency matrix A, (ii) the intrinsic dynamics F of each node, and (iii) the pairwise coupling function G [248].”
Complex network approaches to nonlinear time series analysis (2019, background): “We do not expand the corresponding discussion here, but refer the interested readers to several review papers on this topic [23,38,39].”
Surrogate data for hypothesis testing of physical systems (2018, methodology): “Surrogate data can be applied to the problem of inferring not only that two oscillators are in sync, but also how their phases are coupled by extracting information about their coupling functions [177] over time using dynamical inference (see Section 8.”