Recent Advances in Physical Reservoir Computing: A Review

By Gouhei Tanaka, Toshiyuki Yamane, Jean Benoit Héroux, Ryosho Nakane, Naoki Kanazawa, Seiji Takeda, Hidetoshi Numata, Daiju Nakano, Akira Hirose. Neural Networks, 2018

1,892 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/2da4ce648f0c2f9932ac6b45b6cbdc4f7273d4f2

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In Artificial Intelligence

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition (2019, background): “Reservoir computing is a framework for cognitive computation that may be taken as an extension of ANNs [158], [159].”
Opportunities for neuromorphic computing algorithms and applications (2022, positive): “Reservoir computing in SNNs uses the sparse and recurrent connections with synaptic delays in networks of spiking neurons to cast the input to a spatially and temporally higher dimensional space 51 .”
In-sensor reservoir computing for language learning via two-dimensional memristors (2021, background): “The massive amount of data produced and transmitted by the Internet of Things (IoT) requires new insight into real-time information processing by edge computing devices based on novel materials and architectures ( 1 ).”
Experimental photonic quantum memristor (2021, background): “[45] Gouhei Tanaka, Toshiyuki Yamane, Jean Benoit Héroux, Ryosho Nakane, Naoki Kanazawa, Seiji Takeda, Hidetoshi Numata, Daiju Nakano, and Akira Hirose, “Recent advances in physical reservoir computing: A review,” Neural Networks 115, 100 – 123 (2019).”
Pretrained Transformers as Universal Computation Engines (2021, methodology): “Similarly to the FPT setup and Global Workspace Theory, in reservoir computing (Tanaka et al. 2019) and echo state networks (Jaeger 2001; Jaeger and Haas 2004), a random recurrent network is frozen and only the output readout layer is trained.”
Deep physical neural networks trained with backpropagation (2021, methodology): “21, 22).”