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Proceedings of the 3rd ACM International Conference on Nanoscale Computing and Communication, 2016 Reservoir Computing (RC) is an umbrella term for adaptive computational paradigms that rely on an excitable dynamical system, also called the "reservoir." The paradigms have been shown to be particularly promising for temporal signal processing. RC was also explored as a potential candidate for emerging nanoscale architectures.
Goudarzi, Alireza, Teuscher, Christof
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Deep Reservoir Computing [PDF]
, 2021 This chapter surveys the recent advancements on the extension of Reservoir Computing toward deep architectures, which is gaining increasing research attention in the neural networks community.
Gallicchio C., Micheli A.
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Scientific Reports, 2023 Reservoir computing is a brain heuristic computing paradigm that can complete training at a high speed. The learning performance of a reservoir computing system relies on its nonlinearity and short-term memory ability.
Zhiqiang Liao +5 more
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Recent Advances in Reservoir Computing With A Focus on Electronic Reservoirs [PDF]
, 2020 Reservoir Computing is a subset of recurrent neural networks which can compute temporal-spatial tasks efficiently. In reservoir computing inputs are randomly connected to fixed untrained nodes in the reservoir layer.
Yang Yi +7 more
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Reservoir computing with output feedback [PDF]
, 2011 Reinhart RF. Reservoir computing with output feedback. Bielefeld: Bielefeld University; 2011.A dynamical system approach to forward and inverse modeling is proposed. Forward and inverse models are trained in associative recurrent neural networks that are
Reinhart, René Felix, Reinhart, Felix
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Quantum reservoir computing with a single nonlinear oscillator
Physical Review Research, 2021 Realizing the promise of quantum information processing remains a daunting task given the omnipresence of noise and error. Adapting noise-resilient classical computing modalities to quantum mechanics may be a viable path towards near-term applications in
L. C. G. Govia +4 more
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Physical reservoir computing with dynamical electronics [PDF]
, 2022 Since the advent of data-driven society, mass information generated from human activity and the natural environment has been collected, stored, processed, and then dispersed under conventional von Neumann architecture.
Liang, Xiangpeng
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Regularization by Intrinsic Plasticity and its Synergies with Recurrence for Random Projection Methods [PDF]
, 2012 Neumann K, Emmerich C, Steil JJ. Regularization by Intrinsic Plasticity and its Synergies with Recurrence for Random Projection Methods. Journal of Intelligent Learning Systems and Applications. 2012;4(3):230-246.Neural networks based on high-dimensional
Emmerich, Christian +2 more
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Reservoir computing on the hypersphere [PDF]
International Journal of Modern Physics C, 2017 Reservoir Computing (RC) refers to a Recurrent Neural Network (RNNs) framework, frequently used for sequence learning and time series prediction. The RC system consists of a random fixed-weight RNN (the input-hidden reservoir layer) and a classifier (the hidden-output readout layer).
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Optimizing memory in reservoir computers [PDF]
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2022 A reservoir computer is a way of using a high dimensional dynamical system for computation. One way to construct a reservoir computer is by connecting a set of nonlinear nodes into a network. Because the network creates feedback between nodes, the reservoir computer has memory.
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