  The goal of polyhomeostatic control is to achieve a certain target
distribution of behaviors, in contrast to polyhomeostatic 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.
