自适应网络中的自组织嵌合态
Self-organizing Chimera States in Adaptive Networks
AI总结：
本研究提出一种带乘积耦合的极简自适应网络模型，发现系统可自组织形成自适应嵌合态，其相干集群占比固定，存在多种集体动力学模式且吸引子景观高度多稳态，为相关动力学研究提供了新视角。
AI中文摘要：
我们提出了一种具有乘积形式耦合的极简自适应网络模型，该模型在保留突触可塑性关键特征的同时降低了维度。系统自发自组织形成自适应嵌合态，相同振子通过自适应权重动力学分化为同步与去同步组；这些自适应嵌合态组织成具有固定相干集群占比的分支，展现出平稳、呼吸式与混沌集体动力学间的转变，揭示了集体分岔结构。关键的是，所得吸引子景观具有高度多稳态性：重复的集群重组会产生在相同参数范围内共存且对初始条件敏感依赖的不同动力学路径。
英文摘要：
We propose a minimal adaptive network model with product-form coupling that reduces dimensionality while capturing key features of synaptic plasticity. The system spontaneously self-organizes into adaptive chimera states, where identical oscillators separate into synchronized and desynchronized groups through adaptive weight dynamics. These adaptive chimeras organize into branches with fixed coherent cluster fraction and exhibit transitions between stationary, breathing, and chaotic collective dynamics, revealing a collective bifurcation structure. Crucially, the resulting attractor landscape is highly multistable: repeated cluster reorganizations generate distinct dynamical pathways that coexist within the same parameter regime and depend sensitively on initial conditions.
