The nonlinearity of interactions drives networks of neural oscillators to decoherence at strong coupling

TL;DR

This paper demonstrates that in neural oscillator networks, strong coupling leads to decoherence due to nonlinear interactions, with the transition influenced by network topology, highlighting the importance of nonlinearity in brain dynamics.

Contribution

It reveals that nonlinearity in neural interactions causes decoherence at high coupling, emphasizing the role of network structure in brain activity models.

Findings

Decoherence occurs at strong coupling due to nonlinearity.

Network topology influences the transition to decoherence.

Nonlinear interactions are fundamental in neural oscillator dynamics.

Abstract

While phase oscillators are often used to model neuronal populations, in contrast to the Kuramoto paradigm, strong interactions between brain areas can be associated with loss of synchrony. Using networks of coupled oscillators described by neural mass models, we find that a transition to decoherence at increased coupling strength results from the fundamental nonlinearity, e.g., arising from refractoriness, of the interactions between the nodes. The nonlinearity-driven transition also depends on the connection topology, underlining the role of network structure in shaping brain activity.