Modular organization enhances the robustness of attractor network dynamics
The Institute of Mathematical Sciences, CIT Campus - Taramani, Chennai 600113, India
2 Department of Physics, Birla Institute of Technology & Science - Pilani 333031, India
3 Department of Physics, University of Calcutta - 92 Acharya Prafulla Chandra Road, Kolkata 700009, India
Accepted: 23 March 2011
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 network of 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.
PACS: 87.18.Sn – Neural networks and synaptic communication / 75.10.Nr – Spin-glass and other random models / 89.75.Fb – Structures and organization in complex systems
© EPLA, 2011