Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network in which neurons are randomly connected. Once initialized, the connection strengths remain unchanged. Such a simple structure turns RC into a non-linear dynamical system that maps low-dimensional inputs into a high-dimensional space. The model's rich dynamics, linear separability, and memory capacity then enable a simple linear readout to generate adequate responses for various applications. RC spans areas far beyond machine learning, since it has been shown that the complex dynamics can be realized in various physical hardware implementations and biological devices. This yields greater flexibility and shorter computation time. Moreover, the neuronal responses triggered by the model's dynamics shed light on understanding brain mechanisms that also exploit similar dynamical processes. While the literature on RC is vast and fragmented, here we conduct a unified review of RC's recent developments from machine learning to physics, biology, and neuroscience. We first review the early RC models, and then survey the state-of-the-art models and their applications. We further introduce studies on modeling the brain's mechanisms by RC. Finally, we offer new perspectives on RC development, including reservoir design, coding frameworks unification, physical RC implementations, and interaction between RC, cognitive neuroscience and evolution.

翻译：储层计算（RC）最初应用于时间信号处理，是一种神经元随机连接的循环神经网络。一旦初始化，连接强度就保持不变。这种简单结构将RC转化为一个非线性动力系统，将低维输入映射到高维空间。该模型丰富的动力学特性、线性可分性及记忆容量，使得简单的线性读出层能够为各种应用生成适当的响应。RC的应用领域远超机器学习范畴，因为已证明其复杂动力学可在多种物理硬件实现和生物器件中实现，从而带来更高的灵活性和更短的计算时间。此外，模型动力学触发的神经元响应有助于理解同样利用类似动力学过程的脑机制。尽管RC文献庞大且分散，本文对RC从机器学习到物理学、生物学和神经科学的最新发展进行了统一综述。我们首先回顾早期RC模型，进而调研最先进的模型及其应用，并进一步介绍利用RC模拟脑机制的研究。最后，我们为RC发展提出新视角，包括储层设计、编码框架统一、物理RC实现以及RC与认知神经科学和进化之间的相互作用。