Results 11 to 20 of about 21,729 (293)

All-optical reservoir computing [PDF]

Optics Express, 2012
Reservoir Computing is a novel computing paradigm which uses a nonlinear recurrent dynamical system to carry out information processing. Recent electronic and optoelectronic Reservoir Computers based on an architecture with a single nonlinear node and a delay loop have shown performance on standardized tasks comparable to state-of-the-art digital ...
Duport, Francois, Schneider, B, Smerieri, Anteo, Haelterman, Marc, Massar, Serge   +4 more
openaire   +5 more sources

Passive frustrated nanomagnet reservoir computing [PDF]

Communications Physics, 2023
Reservoir computing (RC) has received recent interest because reservoir weights do not need to be trained, enabling extremely low-resource consumption implementations, which could have a transformative impact on edge computing and in-situ learning where ...
Alexander J. Edwards, Dhritiman Bhattacharya, Peng Zhou, Nathan R. McDonald, Walid Al Misba, Lisa Loomis, Felipe García-Sánchez, Naimul Hassan, Xuan Hu, Md. Fahim Chowdhury, Clare D. Thiem, Jayasimha Atulasimha, Joseph S. Friedman   +12 more
doaj   +3 more sources

Evolutionary aspects of reservoir computing [PDF]

Philosophical Transactions of the Royal Society B: Biological Sciences, 2019
Reservoir computing (RC) is a powerful computational paradigm that allows high versatility with cheap learning. While other artificial intelligence approaches need exhaustive resources to specify their inner workings, RC is based on a reservoir with highly nonlinear dynamics that does not require a fine tuning of its parts. These dynamics project input
L F Seoane
exaly   +5 more sources

RCbench: a unified framework for benchmarking reservoir computing systems [PDF]

Neuromorphic Computing and Engineering
Reservoir computing (RC) is a computational framework where a fixed dynamical reservoir projects an input into a higher-dimensional state that is then analyzed by a readout , which is trained to map the reservoir state into the desired output.
Davide Pilati, Andrea Ceni, Fabio Michieletti, Claudio Gallicchio, Carlo Ricciardi, Gianluca Milano   +5 more
doaj   +2 more sources

Photorefractive reservoir computing [PDF]

Optics Letters
Reservoir computing (RC) is a machine learning (ML) framework that has gained attention in recent years as the interest in alternative computing paradigms has grown. RC allows the utilization of physical systems to solve ML tasks. We demonstrate the use of the nonlinear photorefractive reservoir computer and perform tasks requiring both nonlinearity ...
Alveteg, Sebastian, Sciamanna, Marc, Fuerbach, Alex, Wolfersberger, Delphine   +3 more
openaire   +4 more sources

Attention-enhanced reservoir computing [PDF]

Physical Review Applied
Photonic reservoir computing has been successfully utilized in time-series prediction as the need for hardware implementations has increased. Prediction of chaotic time series remains a significant challenge, an area where the conventional reservoir computing framework encounters limitations of prediction accuracy.
Felix Köster, Kazutaka Kanno, Jun Ohkubo, Atsushi Uchida   +3 more
openaire   +3 more sources

Memristor-based reservoir computing [PDF]

Proceedings of the 2012 IEEE/ACM International Symposium on Nanoscale Architectures, 2012
As feature-size scaling and "Moore's Law" in integrated CMOS circuits further slows down, attention is shifting to computing by non-von Neumann and non-Boolean computing models. Reservoir computing (RC) is a new computing paradigm that allows to harness the intrinsic dynamics of a "reservoir" to perform useful computations.
Manjari S. Kulkarni, Christof Teuscher
openaire   +4 more sources

Deterministic reservoir computing for chaotic time series prediction [PDF]

Scientific Reports
Reservoir Computing was shown in recent years to be useful as efficient to learn networks in the field of time series tasks. Their randomized initialization, a computational benefit, results in drawbacks in theoretical analysis of large random graphs ...
Johannes Viehweg, Constanze Poll, Patrick Mäder   +2 more
doaj   +2 more sources

Multifunctionality in a reservoir computer [PDF]

Chaos: An Interdisciplinary Journal of Nonlinear Science, 2021
Multifunctionality is a well observed phenomenological feature of biological neural networks and considered to be of fundamental importance to the survival of certain species over time. These multifunctional neural networks are capable of performing more than one task without changing any network connections.
Andrew Flynn, Vassilios A. Tsachouridis, Andreas Amann   +2 more
openaire   +4 more sources

Brainwave implanted reservoir computing [PDF]

AIP Advances
This work aims to build a reservoir computing system to recognize signals with the help of brainwaves as the input signals. The brainwave signals were acquired as the participants were listening to the signals.
Li-Yu Chen, Yi-Chun Chen, Jason C. Huang, Sophie Sok, Vincent Armbruster, Chii-Chang Chen   +5 more
doaj   +2 more sources