  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.
