Controlling extreme events in neuronal networks: A single driving signal approach
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.
Growth and citations
This paper is currently showing No growth state computed yet..
Citation metrics and growth state from academic sources (e.g. Semantic Scholar). See About for details.
Cited by (0)
No citing papers yet
Papers that cite this one will appear here once data is available.
View citations page →References (0)
No references in DB yet
References for this paper will appear here once ingested.
Related papers in Chaotic Dynamics
- Thermalization in classical systems with discrete phase space0 citations
- Templex: a bridge between homologies and templates for chaotic attractors0 citations
- Templex-based dynamical units for a taxonomy of chaos0 citations
Growth transitions
No transitions recorded yet
Growth state transitions will appear here once computed.