title: Counting and classifying attractors in high dimensional dynamical systems.
abstract: Randomly connected Boolean networks have been used as mathematical models of neural, genetic, and immune systems. A key quantity of such networks is the number of basins of attraction in the state space. The number of basins of attraction changes as a function of the size of the network, its connectivity and its transition rules. In discrete networks, a simple count of the number of attractors does not reveal the combinatorial structure of the attractors. These points are illustrated in a reexamination of dynamics in a class of random Boolean networks considered previously by Kauffman. We also consider comparisons between dynamics in discrete networks and continuous analogues. A continuous analogue of a discrete network may have a different number of attractors for many different reasons. Some attractors in discrete networks may be associated with unstable dynamics, and several different attractors in a discrete network may be associated with a single attractor in the continuous case. Special problems in determining attractors in continuous systems arise when there is aperiodic dynamics associated with quasiperiodicity of deterministic chaos.
authors.affiliation: Department of Physiology, McGill University, Montreal, Quebec, Canada.
authors.forename: R J
authors.initials: RJ
authors.lastname: Bagley
authors.forename: L
authors.initials: L
authors.lastname: Glass
mesh_terms: D000818:Animals; D003198:Computer Simulation; D008954:Models, Biological; D015233:Models, Statistical; D017711:Nonlinear Dynamics