Controlling extreme events in neuronal networks: A single driving signal approach

TL;DR

This paper demonstrates that controlling a single neuron with a tuned driving signal can effectively suppress extreme events in various neuronal network configurations, offering a scalable control method.

Contribution

The study introduces a single driving signal approach to suppress extreme events in neuronal networks, validated across multiple topologies with demonstrated scalability.

Findings

Suppression of extreme events by influencing one neuron

Breaking phase-locking or frequency coupling achieves control

Control onset occurs earlier with larger response networks

Abstract

We show that in a drive-response coupling framework extreme events are suppressed in the response system by the dominance of a single driving signal. We validate this approach across three distinct response network topologies, namely (i) a pair of coupled neurons, (ii) a monolayer network of N coupled neurons and (iii) a two-layer multiplex network each composed of FitzHugh-Nagumo neuronal units. The response networks inherently exhibit extreme events. Our results demonstrate that influencing just one neuron in the response network with an appropriately tuned driving signal is sufficient to control extreme events across all three configurations. In the two-neuron case, suppression of extreme events occurs due to the breaking of phase-locking between the driving neuron and the targeted response neuron. In the case of monolayer and multiplex networks, suppression of extreme events results from the disruption of protoevent frequency dynamics and a subsequent frequency decoupling of the driven neuron from the rest of the network. We also observe that when the size of the neurons in response network connected to the drive increases, the onset of control occurs earlier indicating a scaling advantage of the method.