Results 11 to 20 of about 101,043 (267)

Physical Implementation of Reservoir Computing through Electrochemical Reaction [PDF]

Advanced Science, 2022
Nonlinear dynamical systems serving reservoir computing enrich the physical implementation of computing systems. A method for building physical reservoirs from electrochemical reactions is provided, and the potential of chemical dynamics as computing ...
Shaohua Kan, Kohei Nakajima, Tetsuya Asai, Megumi Akai‐Kasaya   +3 more
doaj   +5 more sources

A Hopf physical reservoir computer [PDF]

Scientific Reports, 2021
Physical reservoir computing utilizes a physical system as a computational resource. This nontraditional computing technique can be computationally powerful, without the need of costly training.
Md Raf E Ul Shougat, XiaoFu Li, Tushar Mollik, Edmon Perkins   +3 more
doaj   +3 more sources

Recent advances in physical reservoir computing: A review

Neural Networks, 2019
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 ...
Gouhei Tanaka, Toshiyuki Yamane, Jean Benoit Héroux, Ryosho Nakane, Naoki Kanazawa, Seiji Takeda, Hidetoshi Numata, Daiju Nakano, Akira Hirose   +8 more
openaire   +5 more sources

Physical reservoir computing for biomedical applications

Neuromorphic Computing and Engineering
Reservoir computing (RC) is a feedforward computational framework derived from recurrent neural networks that leverages the high-dimensional dynamic behaviors of complex systems for efficient information processing.
Yuqi Ding, Bhavani Prasad Yalagala, Haobo Li, Mohammed Waqas Mughal, Hadi Heidari   +4 more
doaj   +2 more sources

Towards mixed physical node reservoir computing: light-emitting synaptic reservoir system with dual photoelectric output

Light: Science & Applications
Memristor-based physical reservoir computing holds significant potential for efficiently processing complex spatiotemporal data, which is crucial for advancing artificial intelligence.
Minrui Lian, Changsong Gao, Zhenyuan Lin, Liuting Shan, Cong Chen, Yi Zou, Enping Cheng, Changfei Liu, Tailiang Guo, Wei Chen, Huipeng Chen   +10 more
doaj   +2 more sources

Embodying physical computing into soft robots. [PDF]

Nat Commun
Softening and onboarding computers and controllers is one of the final frontiers in soft robotics towards their robustness and intelligence for everyday use. In this regard, embodying soft and physical computing presents exciting potential.
Wang J, Zhou Z, Kahak A, Li S.
europepmc   +2 more sources

Physics-based approach to developing physical reservoir computers

Physical Review Research
Reservoir computing leverages the dynamic properties of a fixed, randomly connected neural network to facilitate simplified training and enhanced computational efficiency. Many forms of physical reservoir computers have been proposed.
Vahideh Shirmohammadli, Behraad Bahreyni
doaj   +2 more sources

Magneto-Ionic Physical Reservoir Computing in Perpendicularly Magnetized Heterostructures. [PDF]

Nano Lett
Recent progresses in magnetoionics offer exciting potentials to leverage its non-linearity, short-term memory, and energy-efficiency to uniquely advance the field of physical reservoir computing. In this work, we experimentally demonstrate the classification of temporal data using a magneto-ionic (MI) heterostructure.
Rajib MM, Bhattacharya D, Jensen CJ, Chen G, Chowdhury FF, Sarker S, Liu K, Atulasimha J.   +7 more
europepmc   +3 more sources

Task-adaptive physical reservoir computing. [PDF]

Nat Mater, 2023
AbstractReservoir computing is a neuromorphic architecture that may offer viable solutions to the growing energy costs of machine learning. In software-based machine learning, computing performance can be readily reconfigured to suit different computational tasks by tuning hyperparameters.
Lee O, Wei T, Stenning KD, Gartside JC, Prestwood D, Seki S, Aqeel A, Karube K, Kanazawa N, Taguchi Y, Back C, Tokura Y, Branford WR, Kurebayashi H.   +13 more
europepmc   +7 more sources

Spatial analysis of physical reservoir computers

Physical Review Applied, 2023
7 Pages, 5 ...
Jake Love, Robin Msiska, Jeroen Mulkers, George Bourianoff, Jonathan Leliaert, Karin Everschor-Sitte   +5 more
openaire   +3 more sources