标题： 模块化组织增强了吸引子网络动力学的鲁棒性 显示英文标题

标题： Modular organization enhances the robustness of attractor network dynamics

摘要： 模块化组织特征体现在许多自然发生的复杂网络中，包括大脑。 在本文中，我们表明模块化结构可能负责增加此类系统某些动态状态的鲁棒性。 在一个具有阈值激活二进制元件的神经网络模型中，我们观察到，对应于使用学习规则嵌入的模式的吸引子的盆地，在最优模块化时占据相空间的最大体积。 同时，由于模块之间的协同动力学，这些吸引子的收敛时间减少。 模块化在增加系统某些理想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性提供线索。 显示英文摘要

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.