  Reservoir computing is a computational framework suited for
temporal/sequential data processing. It is derived from several recurrent
neural network models, including echo state networks and liquid state machines.
A reservoir computing system consists of a reservoir for mapping inputs into a
high-dimensional space and a readout for pattern analysis from the
high-dimensional states in the reservoir. The reservoir is fixed and only the
readout is trained with a simple method such as linear regression and
classification. Thus, the major advantage of reservoir computing compared to
other recurrent neural networks is fast learning, resulting in low training
cost. Another advantage is that the reservoir without adaptive updating is
amenable to hardware implementation using a variety of physical systems,
substrates, and devices. In fact, such physical reservoir computing has
attracted increasing attention in diverse fields of research. The purpose of
this review is to provide an overview of recent advances in physical reservoir
computing by classifying them according to the type of the reservoir. We
discuss the current issues and perspectives related to physical reservoir
computing, in order to further expand its practical applications and develop
next-generation machine learning systems.
