Synchrony in networks of coupled non-smooth dynamical systems: extending the master stability function

Coombes, Stephen and Thul, Ruediger (2016) Synchrony in networks of coupled non-smooth dynamical systems: extending the master stability function. European Journal of Applied Mathematics . pp. 1-19. ISSN 1469-4425

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Abstract

The master stability function is a powerful tool for determining synchrony in high-dimensional networks of coupled limit cycle oscillators. In part, this approach relies on the analysis of a low-dimensional variational equation around a periodic orbit. For smooth dynamical systems, this orbit is not generically available in closed form. However, many models in physics, engineering and biology admit to non-smooth piece-wise linear caricatures, for which it is possible to construct periodic orbits without recourse to numerical evolution of trajectories. A classic example is the McKean model of an excitable system that has been extensively studied in the mathematical neuroscience community. Understandably, the master stability function cannot be immediately applied to networks of such non-smooth elements. Here, we show how to extend the master stability function to non-smooth planar piece-wise linear systems, and in the process demonstrate that considerable insight into network dynamics can be obtained. In illustration, we highlight an inverse period- doubling route to synchrony, under variation in coupling strength, in globally linearly coupled networks for which the node dynamics is poised near a homoclinic bifurcation. Moreover, for a star graph, we establish a mechanism for achieving so-called remote synchronisation (where the hub oscillator does not synchronise with the rest of the network), even when all the oscillators are identical. We contrast this with node dynamics close to a non-smooth Andronov–Hopf bifurcation and also a saddle node bifurcation of limit cycles, for which no such bifurcation of synchrony occurs.

Item Type: Article
Keywords: General Applied Mathematics, Synchronisation, Non-Smooth Equations, Complex Networks, Neural Networks
Schools/Departments: University of Nottingham UK Campus > Faculty of Science > School of Mathematical Sciences
Identification Number: https://doi.org/10.1017/S0956792516000115
Depositing User: Thul, Ruediger
Date Deposited: 16 Jun 2016 12:53
Last Modified: 18 Sep 2016 19:23
URI: http://eprints.nottingham.ac.uk/id/eprint/34103

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