How neural network structure alters the brain’s self-organized criticality

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Abstract

In recent years, the “brain critical hypothesis” has been proposed in the fields of complex systems science and statistical physics, suggesting that the brain acquires functions such as information processing capabilities near the critical point, which lies at the boundary between disorder and order. As a mechanism for maintaining this critical state, a feedback system called “self-organized criticality (SOC)” has been proposed, where parameters related to brain function, such as synaptic plasticity, are maintained by internal rules without external adjustments. Additionally, the structure of neural networks plays an important role in information processing, with healthy neural networks being characterized by properties such as small-worldness, scale-freeness, and modularity. However, it has also been pointed out that these properties may be lacking in cases of neurological disorders. In this study, we used a mathematical model to investigate the possibility that differences in neural network structures could lead to brain dysfunction through SOC. As a result, it became clear that the synaptic plasticity conditions that maximize information processing capabilities vary depending on the network structure. Notably, when the network possesses only a scale-free structure, a phenomenon known as the Dragon king—associated with abnormal neural activity—was observed. These findings suggest that the maintenance of neural dynamics equilibrium differs depending on the structural characteristics of the neural network, and that in structures with hub nodes, such as scale-free networks, imbalances in neural dynamics may occur, potentially negatively impacting brain function.

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