用物理系统实现神经网络，仅训练输出层即可。
An introduction to reservoir computing
- 利用高维循环网络，只训练最后一层参数。
- 已成功应用于电子、光子、自旋电子等物理系统。
- 适合研究物理实现的神经网络，尤其对量子计算初学者友好。
目前，人们越来越关注在物理系统中实现人工神经网络。其中一大挑战是，此类网络难以训练，因为训练需改变物理参数而非计算机程序中的系数。为此，储备池计算（reservoir computing）被广泛应用——它采用高维递归网络，并仅训练最终输出层。本文介绍储备池计算的基本概念，展示来自电子学、光子学、自旋电子学、力学及生物学的重要物理实现案例，并简要讨论量子储备池计算的发展前景。
原文摘要 · Abstract (English)
There is a growing interest in the development of artificial neural networks that are implemented in a physical system. A major challenge in this context is that these networks are difficult to train since training here would require a change of physical parameters rather than simply of coefficients in a computer program. For this reason, reservoir computing, where one employs high-dimensional recurrent networks and trains only the final layer, is widely used in this context. In this chapter, I introduce the basic concepts of reservoir computing. Moreover, I present some important physical implementations coming from electronics, photonics, spintronics, mechanics, and biology. Finally, I provide a brief discussion of quantum reservoir computing.
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