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

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.