Community modularity structure promotes the evolution of phase clusters and chimeralike states.

Abstract

Community modularity structure is widely observed across various brain scales, reflecting a balance between information processing efficiency and neural wiring metabolic efficiency. Revealing the relationship between community structure and brain function facilitates our further understanding of the brain. Here, we construct an adaptive neural network (ANN) consisting of leaky integrate-and-fire neurons with adaptivity governed by spike-time-dependent plasticity rules. The ANN demonstrates diverse dynamic collective behaviors, including traveling waves dominated by initial states, phase-cluster formations, and chimeralike states. In addition to functional clustering, ANN spontaneously organizes into community structures characterized by densely interconnected modules with sparse interconnections. Neurons within modules synchronize, while those across modules remain asynchronous, forming phase-cluster states. By encoding neural rhythms, the ANN segments into asynchronous and synchronous structural modules, leading to chimeralike states. These findings provide further evidence supporting the perspective that function emerges from structure and that structure is influenced by function in complex dynamic processes.