模块化组织增强了吸引子网络动力学的鲁棒性
Modular organization enhances the robustness of attractor network dynamics
摘要：
模块化组织是自然界中许多复杂网络的特征，包括大脑。在本文中，我们表明，模块化结构可能是负责增加某些动态状态的鲁棒性，这样的系统。在阈值激活的二进制元素的神经网络模型中，我们观察到，吸引子的盆地，对应于已被嵌入使用的学习规则的模式，占据相空间中的最大体积在最佳模块化。同时，收敛到这些吸引子的时间减少，由于模块之间的合作动态。模块性在增加系统的某些期望吸引子的全局稳定性中的作用可能为它在自然系统中的进化和普遍存在提供线索。
英文摘要：
Modular organization characterizes many complex networks occurring in nature, including the brain. In this paper we show that modular structure may be responsible for increasing the robustness of certain dynamical states of such systems. In a neural network model with threshold-activated binary elements, we observe that the basins of attractors, corresponding to patterns that have been embedded using a learning rule, occupy maximum volume in phase space at an optimal modularity. Simultaneously, the convergence time to these attractors decreases as a result of cooperative dynamics between the modules. The role of modularity in increasing global stability of certain desirable attractors of a system may provide a clue to its evolution and ubiquity in natural systems.
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