Modular organization enhances the robustness of attractor network dynamics

Abstract

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.

6 pages, 4 figures

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Cited by in corpus (4)

• Development of modularity in the neural activity of children's brains
• Mesoscopic architecture enhances communication across the Macaque connectome revealing structure-function correspondence in the brain
• Complex ordering in spin networks: Critical role of adaptation rate for dynamically evolving interactions
• Multiple dynamical time-scales in networks with hierarchically nested modular organization