Self-organized chaos through polyhomeostatic optimization
Disordered Systems and Neural Networks
2013-05-29 v2 Neurons and Cognition
Abstract
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.
Keywords
Cite
@article{arxiv.1001.0663,
title = {Self-organized chaos through polyhomeostatic optimization},
author = {Dimitrije Markovic and Claudius Gros},
journal= {arXiv preprint arXiv:1001.0663},
year = {2013}
}