Mitigation of extreme events in an excitable system

Abstract

Formulating mitigation strategies is one of the main aspect in the dynamical study of extreme events. Apart from the effective control, easy implementation of the devised tool should also be given importance. In this work, we analyze the mitigation of extreme events in a coupled FitzHugh-Nagumo (FHN) neuron model utilizing an easily implementable constant bias analogous to a constant DC stimulant. We report the route through which the extreme events gets mitigated in $Two$, $Three$ and $N-$coupled FHN systems. In all the three cases, extreme events in the observable $\bar{x}$ gets suppressed. We confirm our results with the probability distribution function of peaks, $d_{max}$ plot and probability plots. Here $d_{max}$ is a measure of number of standard deviations that crosses the average amplitude corresponding to ${\bar{x}}_{max}$. Interestingly, we found that constant bias suppresses the extreme events without changing the collective frequency of the system.

19 pages, 25 figures

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Cited by in corpus (3)

• Propagation of extreme events in multiplex neuronal networks
• Heterogeneous noise-induced extreme events and synchronization in a globally coupled network of FitzHugh-Nagumo oscillators
• Emergence of rogue-like waves in a reaction-diffusion system: Stochastic output from deterministic dissipative dynamics