Physical reservoir computers that can adapt to perform different tasks
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OpenAlex topics: Neural Networks and Reservoir Computing · Advanced Memory and Neural Computing · Neural Networks and Applications
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References (5)
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1. Marković, D., Mizrahi, A., Querlioz, D. & Grollier, J. Physics for neuromorphic computing. Nat. Rev. Phys. 2, 499–510 (2020). This review article gives an overview of neuromorphic computing, including reservoir computing.10.1038/s42254-020-0208-2 ↗
2. Jaeger, H. The “Echo State” Approach to Analysing and Training Recurrent Neural Networks — With an Erratum Note GMD Technical Report 148 (German National Research Centre for Information Technology, 2001). This paper introduces the echo-state network, which provides one of the foundations of reservoir computing.
3. Maass, W., Natschläger, T. & Markram, H. Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput. 14, 2531–2560 (2002). This paper proposes liquid-state machines, which are one of the foundations of reservoir computing.10.1162/089976602760407955 ↗
4. Tanaka, G. et al. Recent advances in physical reservoir computing: a review. Neural Netw. 115, 100–123 (2019). This review article presents an overview of different physical systems as reservoirs.10.1016/j.neunet.2019.03.005 ↗
5. Lee, O. et al. Tunable gigahertz dynamics of low-temperature skyrmion lattice in a chiral magnet. J. Phys. Condens. Matter 34, 095801 (2021). This paper outlines protocols for nucleating metastable skyrmions in Cu2OSeO3 by field cycling.10.1088/1361-648X/ac3e1c ↗