Self-organized chaos through polyhomeostatic optimization.
The goal of polyhomeostatic control is to achieve a certain target distribution of behaviors, in contrast to homeostatic regulation, which aims at stabilizing a steady-state dynamical state. We consider polyhomeostasis for individual and networks of firing-rate neurons, adapting to achieve target distributions of firing rates maximizing information entropy. We show that any finite polyhomeostatic adaption rate destroys all attractors in Hopfield-like network setups, leading to intermittently bursting behavior and self-organized chaos. The importance of polyhomeostasis to adapting behavior in general is discussed.
Markovic
D
D
Institute for Theoretical Physics, Johann Wolfgang Goethe University, Frankfurt am Main, Germany.
Gros
Claudius
C
D006706:Homeostasis; D008954:Models, Biological; D009474:Neurons / Q000378:metabolism; D017711:Nonlinear Dynamics; D013997:Time Factors