The dance of neurons: exploring nonlinear dynamics in brain networks (Q6591808)

Starting from a large-scale network of threshold Hodgkin-Huxley style neurons, the average nonlinear dynamics implicitly following from the Wilson-Cowan assumptions is formulated. More precisely, in this article, the authors investigate the influence of biophysical and structural properties on the complexity of neural dynamics at the microscale level and its relationship with the macroscopic Wilson-Cowan model. The simulations of the temporal profiles reveal dependency on the binary state of interacting subpopulations and the random property of structural network at the transition points, when different synaptic weights are considered. It is shown that finite-size effects kick the system in a state of irregular modes to evolve differently from predictions of the original Wilson-Cowan reference. Additionally, the authors report that the complexity and temporal diversity of neural dynamics, especially in terms of limit cycle trajectory, and synchronization can be induced by either small heterogeneity in the degree of various types of local excitatory connectivity or considerable diversity in the external drive to the excitatory pool. The nonlinear dynamic network model is discussed in detail in Section 5.