Exploiting chaotic dynamics as deep neural networks

Chaos presents complex dynamics arising from nonlinearity and a sensitivity to initial states. These characteristics suggest a depth of expressivity that underscores their potential for advanced computational applications. However, strategies to effectively exploit chaotic dynamics for information processing have largely remained elusive. In this study, we reveal that the essence of chaos can be found in various state-of-the-art deep neural networks. Drawing inspiration from this revelation, we propose a novel method that directly leverages chaotic dynamics for deep learning architectures. Our approach is systematically evaluated across distinct chaotic systems. In all instances, our framework presents superior results to conventional deep neural networks in terms of accuracy, convergence speed, and efficiency. Furthermore, we find an active role of transient chaos formation in our framework. Collectively, this study opens up a new path for the integration of chaos, which has long been overlooked in information processing, and provides insights into the potential fusion of chaotic dynamics within the domains of machine learning and neuromorphic computation.