Recent research progress in reservoir computing

Reservoir computing (RC) is a novel computing framework that simplifies the complexity of recurrent neural networks (RNNs). By fixing the dynamic connection weights of reservoir nodes and only training the weights of the output layer, it significantly reduces the training cost and difficulty of data prediction. This paper systematically reviews the origin and evolution of the RC method, focuses on analyzing the principles and improvement directions of RC over the past five years, and their variant models. It also provides an overview of their recent applications in time series prediction, memristor technology and other fields and forecasts future research trends based on current limitations.