Generic Adaptation by Fast Chaotic Exploration and Slow Feedback Fixation

TL;DR

This paper proposes a generic adaptation mechanism combining fast chaotic exploration with slow feedback fixation, inspired by biological processes, which enhances adaptation efficiency and is relevant to cellular and neural network optimization.

Contribution

It introduces a novel model integrating fast chaotic dynamics with slow feedback, inspired by biological adaptation, and demonstrates its effectiveness through extensive simulations.

Findings

Fast chaotic dynamics enable global search for adapted states.

The mechanism's effectiveness increases with system size.

Relevance to cellular adaptation and neural network optimization is discussed.

Abstract

Living systems adapt to various environmental conditions by changing their internal states. Inspired by gene expression and epigenetic modification dynamics, we herein propose a generic mechanism for adaptation by combining fast oscillatory dynamics and a slower feedback fixation process. Through extensive model simulations, we reveal that fast chaotic dynamics serve as global searching for adapted states fixed by slower dynamics. The mechanism improves as the number of elements is increased. Relevance to cellular adaptation and optimization in artificial neural networks is also discussed herein.