Survey on reservoir computing

A novel training method for recurrent neural networks, which is called reservoir computing, was proposed with the purpose of dealing with difficulties in the training of the traditional recurrent neural networks. The main idea of the reservoir computing is training only parts of the connection weights of the networks, and generating the rest parts randomly. The connection weights generated randomly remain unchanged during the training process. Then training process of the network can be carried out by solving a linear regression problem. The reservoir can be considered as a temporal kernel function which extends the applications of the reservoir computing. In fact, the reservoir computing is not only a modification of the training algorithm to recurrent neural networks. In this paper, we firstly introduce the mathematical model of the reservoir computing and analyze the current related researches and applications in detail in the view of reservoir adaption which has attracted much interest of the researchers recently.