Complex networks with large numbers of labelable attractors
Department of Physics, Beijing Normal University - Beijing 100875, China
2 Nonlinear Research Institute, Baoji University of Arts and Sciences - Baoji 721007, China
Accepted: 11 July 2011
Information storage in many functional subsystems of the brain is regarded by theoretical neuroscientists to be related to attractors of neural networks. The number of attractors is large and each attractor can be temporarily represented or suppressed easily by corresponding external stimulus. In this letter, we discover that complex networks consisting of excitable nodes have similar fascinating properties of coexistence of large numbers of oscillatory attractors, most of which can be labeled with a few nodes. According to a simple labeling rule, different attractors can be identified and the number of labelable attractors can be predicted from the analysis of network topology. With the cues of the labeling association, these attractors can be conveniently retrieved or suppressed on purpose.
PACS: 89.75.Fb – Structures and organization in complex systems / 89.75.Kd – Patterns / 05.65.+b – Self-organized systems
© EPLA, 2011