Structural Insights Into the Dynamic Evolution of Neuronal Networks as Synaptic Density Decreases - Yuan, Ye; Liu, Jian; Zhao, Peng; Xing, Fu; Huo, Hong; Fang, Tao (Frontiers in Neuroscience, 2019)
Abstract The human brain is thought to be an extremely complex but efficient computing engine, processing vast amounts of information from a changing world. The decline in the synaptic density of neuronal networks is one of the most important characteristics of brain development, which is closely related to synaptic pruning, synaptic growth, synaptic plasticity, and energy metabolism. However, because of technical limitations in observing large-scale neuronal networks dynamically connected through synapses, how neuronal networks are organized and evolve as their synaptic density declines remains unclear. Here, by establishing a biologically reasonable neuronal network model, we show that despite a decline in the synaptic density, the connectivity and efficiency of neuronal networks can be improved. Importantly, by analysing the degree distribution, we also find that both the scale-free characteristic of neuronal networks and the emergence of hub neurons rely on the spatial distance between neurons. These findings may promote our understanding of neuronal networks in the brain and have guiding significance for the design of neuronal network models. Keywords: evolving network model, synaptic density, network efficiency, network connectivity, scale-free network