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Abstract

It is a fundamental challenge to understand how brain function is related to its functional and structural organization, i.e., what shapes the neuronal activity patterns observed across scales that define cognitive and behavioral processes, as well as their breakdown in mental health disorders. The dynamics of neuronal networks strongly depends on intrinsic properties of the neuro-anatomical connectome and the functional relationships among neurons, and this goes beyond the connectivity matrix. In particular, the adaptation of the strengths of the synaptic connections through synaptic plasticity, the evolution of the functional connectivity in time, the inevitable time-delays resulting from both neurophysiological time constants and finite propagation velocity, noise, and inherent inhomogeneities play key roles in the emergent behavior of neuronal systems across spatial and temporal scales. A detailed characterization of these effects on the collective dynamics of neuronal networks may thus provide the means for studying the link between functional and structural connectivity and brain function. This Research Topic focuses on the structure-function relationship in neuronal networks at different temporal and spatial scales. The latter can range from fast-spiking and bursting dynamics of individual neurons, mean collective activity of neuronal populations to slow and ultra-slow fluctuations of neuronal and metabolic activity at the whole-brain scale. Special attention will be paid to the modeling of the neuronal plasticity (or adaptivity), impacts of time delays in coupling and intrinsic activity, and effects of noise or stochastic perturbations on individual and collective neuronal dynamics. The goal of this Research Topic is to collect a wide spectrum of theoretical, computational, and experimental articles, which introduce recent advances in the modeling and analysis of the interplay between the parameters that define the network structure and the repertoire of dynamical regimes of neuronal networks. The close comparison of theoretical/simulation results to empirical brain recordings may contribute to elucidate the observed phenomena from the perspective of complex networks and nonlinear dynamics. Such a collection might contribute to a better understanding of how the brain connectome structure can shape the neuronal activity in space and time, ultimately leading to cognition and behavior.