Synchronization in multiplex models of neuron-glial systems: Small-world topology and inhibitory coupling

In this work, we investigate the impact of mixed coupling on synchronization in a multiplex oscillatory network. The network mimics the neural-glial systems by incorporating interacting slow ( glial ) and fast ( neural ) oscillatory layers. Connections between the glial elements form a regular periodic structure, in which each element is connected to the eight other neighbor elements, whereas connections among neural elements are represented by Watts-Strogatz networks (from regular and small-world to random Erdos-Renyi graph) with a matching mean node degree. We find that the random rewiring toward small-world topology readily yields the dynamics close to that exhibited for a completely random graph, in particular, leading to coarse-graining of dynamics, suppressing multi-stability of synchronization regimes, and the onset of Kuramoto-type synchrony in both layers. The duration of transient dynamics in the system measured by relaxation times is minimized with the increase of random connections in the neural layer, remaining substantial only close to synchronization-desynchronization transitions. Inhibitory interactions in the neural subnetwork layer undermine synchronization; however, the strong coupling with the glial layer overcomes this effect.

Automated summary

This study investigates the impact of mixed coupling on synchronization in a multiplex oscillatory network that mimics neural-glial systems. The results show that randomness in connections leads to dynamics similar to a completely random graph, with fine-tuning of synchronization and Kuramoto-type synchrony observed. Inhibitory interactions in the neural subnetwork layer weaken synchronization, but strong coupling with the glial layer mitigates this effect.