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 found an active role of transient chaos formation in our scheme. Collectively, this study offers a new path for the integration of chaos, which has long been overlooked in information processing, and provides insights into the prospective fusion of chaotic dynamics within the domains of machine learning and neuromorphic computation.

翻译：混沌现象呈现出由非线性及对初始状态的敏感性引发的复杂动力学。这些特性暗示了其表现力的深度，凸显了它们在高级计算应用中的潜力。然而，有效利用混沌动力学进行信息处理的策略在很大程度上仍然难以实现。在本研究中，我们揭示了混沌的本质可以在各种最先进的深度神经网络中找到。受此发现的启发，我们提出了一种直接利用混沌动力学进行深度学习架构设计的新方法。我们的方法在多个不同的混沌系统中进行了系统评估。在所有实例中，我们的框架在精度、收敛速度和效率方面均优于传统的深度神经网络。此外，我们发现了瞬态混沌形成在我们的方案中发挥的积极作用。总的来说，这项研究为长期在信息处理中被忽视的混沌整合提供了新路径，并为混沌动力学在机器学习与神经形态计算领域的潜在融合提供了见解。