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Modular organization characterizes many complex networks occurring in nature,including the brain. In this paper we show that modular structure may beresponsible for increasing the robustness of certain dynamical states of suchsystems. In a neural network model with threshold-activated binary elements, weobserve that the basins of attractors, corresponding to patterns that have beenembedded using a learning rule, occupy maximum volume in phase space at anoptimal modularity. Simultaneously, the convergence time to these attractorsdecreases as a result of cooperative dynamics between the modules. The role ofmodularity in increasing global stability of certain desirable attractors of asystem may provide a clue to its evolution and ubiquity in natural systems.

Modular organization enhances the robustness of attractor network dynamics