Reservoir Computing
Reservoir Computing is a computational framework for training recurrent neural networks in which a fixed, randomly initialized dynamical system, known as the reservoir, projects input signals into a high-dimensional state space, enabling efficient learning through simple linear readout mechanisms.
1.1K
Publications
69.1K
Citations
3K
Authors
902
Institutions
Publications per year
2017–2026
826
1.1K
An experimental unification of reservoir computing methods
D. Verstraeten, Benjamin Schrauwen, Michiel D’Haene et al. · Neural Networks · 2007 · 1.1K citations
Reservoir computing using dynamic memristors for temporal information processing
Chao Du, Fuxi Cai, Mohammed A. Zidan et al. · Nature Communications · 2017 · 993 citations · Full text
1–5 of 1.1K
3K
27
h-index
Institute for Cross-Disciplinary Physics and Complex Systems, Spain
25 publications · 5K citations
24
h-index
20
h-index
20
h-index
Institute for Cross-Disciplinary Physics and Complex Systems, Spain
20 publications · 4.1K citations
19
h-index
1–5 of 3K
902

Ghent University
Ghent, Belgium
36 h-index
245 publications
32.1K citations
Institute for Cross-Disciplinary Physics and Complex Systems
Palma, Spain
31 h-index
123 publications
17.8K citations
The University of Tokyo
Tokyo, Japan
31 h-index
134 publications
6.8K citations

Centre National de la Recherche Scientifique
Paris, France
25 h-index
104 publications
11.1K citations
Universitat de les Illes Balears
Palma, Spain
25 h-index
91 publications
14.9K citations
Université Libre de Bruxelles
Brussels, Belgium
18 h-index
69 publications
11.1K citations
University of Maryland, College Park
College Park, United States
17 h-index
68 publications
15.8K citations

Chinese Academy of Sciences
Beijing, China
17 h-index
125 publications
5K citations
1–8 of 902
59 publications · 3.9K citations
32
h-index
34 publications · 7.2K citations
28
h-index
30 publications · 1.9K citations
26
h-index
36 publications · 3.4K citations
24
h-index
34 publications · 3K citations
23
h-index
1–5