Self-organized chaos through polyhomeostatic optimization

TL;DR

This paper demonstrates that polyhomeostatic adaptation in neural networks leads to self-organized chaos and bursting behavior by destroying attractors, highlighting its role in adaptive neural dynamics.

Contribution

It introduces a novel perspective on polyhomeostatic control, showing how it induces chaos in neural networks by disrupting attractor states.

Findings

Polyhomeostatic adaptation destroys attractors in neural networks.

It induces intermittently bursting behavior and chaos.

The approach maximizes information entropy of firing rates.

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.