Controlling Neuronal Noise Using Chaos Control

TL;DR

This paper demonstrates that chaos control techniques can be applied to non-chaotic and stochastic neuronal models, challenging the assumption that such systems must be chaotic for these methods to be effective.

Contribution

It reveals that chaos control methods can be used on non-chaotic and stochastic systems, questioning the reliability of chaos identification in physiological experiments.

Findings

Chaos criteria can falsely classify noise-driven systems as chaotic.

Chaos control yields similar results in non-chaotic and stochastic models.

Challenges the assumption that neuronal networks must be chaotic for chaos control.

Abstract

Chaos control techniques have been applied to a wide variety of experimental systems, including magneto-elastic ribbons, lasers, chemical reactions, arrhythmic cardiac tissue, and spontaneously bursting neuronal networks. An underlying assumption in all of these studies is that the system being controlled is chaotic. However, the identification of chaos in experimental systems, particularly physiological systems, is a difficult and often misleading task. Here we demonstrate that the chaos criteria used in a recent study can falsely classify a noise-driven, non-chaotic neuronal model as being chaotic. We apply chaos control, periodic pacing, and anticontrol to the non-chaotic model and obtain results which are similar to those reported for apparently chaotic, {\em in vitro} neuronal networks. We also obtain similar results when we apply chaos control to a simple stochastic system. These novel findings challenge the claim that the aforementioned neuronal networks were chaotic and suggest that chaos control techniques can be applied to a wider range of experimental systems than previously thought.