title: Balancing robustness against the dangers of multiple attractors in a Hopfield-type model of biological attractors.
abstract: BACKGROUND
  Many chronic human diseases are of unclear origin, and persist long beyond any known insult or instigating factor. These diseases may represent a structurally normal biologic network that has become trapped within the basin of an abnormal attractor.
  
  
  RESULTS
  We used the Hopfield net as the archetypical example of a dynamic biological network. By progressively removing the links of fully connected Hopfield nets, we found that a designated attractor of the nets could still be supported until only slightly more than 1 link per node remained. As the number of links approached this minimum value, the rate of convergence to this attractor from an arbitrary starting state increased dramatically. Furthermore, with more than about twice the minimum of links, the net became increasingly able to support a second attractor.
  
  
  CONCLUSIONS
  We speculate that homeostatic biological networks may have evolved to assume a degree of connectivity that balances robustness and agility against the dangers of becoming trapped in an abnormal attractor.
authors.affiliation: Division of Sleep Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: Ron C
authors.initials: RC
authors.lastname: Anafi
authors.forename: Jason H T
authors.initials: JH
authors.lastname: Bates
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D030342:Genetic Diseases, Inborn / Q000378:metabolism; D006706:Homeostasis; D006801:Humans; D007700:Kinetics; D008954:Models, Biological; D015233:Models, Statistical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012984:Software; D049490:Systems Biology