Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2019 Jul:115:100-123.
doi: 10.1016/j.neunet.2019.03.005. Epub 2019 Mar 20.

Recent advances in physical reservoir computing: A review

Gouhei Tanaka  1 , Toshiyuki Yamane  2 , Jean Benoit Héroux  2 , Ryosho Nakane  3 , Naoki Kanazawa  2 , Seiji Takeda  2 , Hidetoshi Numata  2 , Daiju Nakano  2 , Akira Hirose  3
Affiliations
Free article
Review

Recent advances in physical reservoir computing: A review

Gouhei Tanaka et al. Neural Netw. 2019 Jul.
Free article

Abstract

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.

Keywords: Machine learning; Neural networks; Neuromorphic device; Nonlinear dynamical systems; Reservoir computing.

PubMed Disclaimer

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

LinkOut - more resources