Liao Xuhong, Xia Qinzhi, Qian Yu, Zhang Lisheng, Hu Gang, Mi Yuanyuan
Department of Physics, Beijing Normal University, Beijing 100875, China.
Phys Rev E Stat Nonlin Soft Matter Phys. 2011 May;83(5 Pt 2):056204. doi: 10.1103/PhysRevE.83.056204. Epub 2011 May 4.
Oscillatory dynamics of complex networks has recently attracted great attention. In this paper we study pattern formation in oscillatory complex networks consisting of excitable nodes. We find that there exist a few center nodes and small skeletons for most oscillations. Complicated and seemingly random oscillatory patterns can be viewed as well-organized target waves propagating from center nodes along the shortest paths, and the shortest loops passing through both the center nodes and their driver nodes play the role of oscillation sources. Analyzing simple skeletons we are able to understand and predict various essential properties of the oscillations and effectively modulate the oscillations. These methods and results will give insights into pattern formation in complex networks and provide suggestive ideas for studying and controlling oscillations in neural networks.
复杂网络的振荡动力学近来备受关注。在本文中,我们研究由可激发节点组成的振荡复杂网络中的模式形成。我们发现,对于大多数振荡而言,存在一些中心节点和小骨架。复杂且看似随机的振荡模式可被视为从中心节点沿最短路径传播的组织良好的靶波,并且同时穿过中心节点及其驱动节点的最短回路起到振荡源的作用。通过分析简单骨架,我们能够理解和预测振荡的各种基本特性,并有效调节振荡。这些方法和结果将为复杂网络中的模式形成提供见解,并为研究和控制神经网络中的振荡提供启发性思路。