Solitary states in multiplex neural networks: Onset and vulnerability

We investigate solitary states in a two-layer multiplex network of FitzHugh-Nagumo neurons in the oscillatory regime. We demonstrate how solitary states can be induced in a multiplex network consisting of two non-identical layers. More specifically, we show that these patterns can be introduced via weak multiplexing into a network that is fully synchronized in isolation. We show that this result is robust under variations of the inter-layer coupling strength and largely independent of the choice of initial conditions. Moreover, we study the vulnerability of solitary states with respect to changes in the inter-layer topology. In more detail, we remove links that connect two solitary nodes of each layer and evaluate the resulting pattern. We find a highly non-trivial dependence of the survivability of the solitary states on topological (position in the network) and dynamical (phase of the oscillation) characteristics. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This study investigates solitary states in a two-layer multiplex network of FitzHugh-Nagumo neurons and demonstrates their induction in a non-identical multiplex network. The study also shows the robustness of these states under changes in coupling strength and topology, highlighting their dependence on network position and oscillation phase.