The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Fully analogue photonic reservoir computer
Open accessAbstract
Introduced a decade ago, reservoir computing is an efficient approach for signal processing. State of the art capabilities have already been demonstrated with both computer simulations and physical implementations. If photonic reservoir computing appears to be promising a solution for ultrafast nontrivial computing, all the implementations presented up to now require digital pre or post processing, which prevents them from exploiting their full potential, in particular in terms of processing speed. We address here the possibility to get rid simultaneously of both digital pre and post processing. The standalone fully analogue reservoir computer resulting from our endeavour is compared to previous experiments and only exhibits rather limited degradation of performances. Our experiment constitutes a proof of concept for standalone physical reservoir computers.
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Cites
Maass, W., Natschläger, T., & Markram, H. (2002) · Neural Computation · doi:10.1162/089976602760407955 · Cited by 4,025
Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication
Jaeger, H., & Haas, H. (2004) · Science · doi:10.1126/science.1091277 · Cited by 3,811
Reservoir computing approaches to recurrent neural network training
Lukoševičius, M., & Jaeger, H. (2009) · Computer Science Review · doi:10.1016/j.cosrev.2009.03.005 · Cited by 2,956
Optimization and applications of echo state networks with leaky- integrator neurons
Jaeger, H., Lukoševičius, M., Popovici, D., & Siewert, U. (2007) · Neural Networks · doi:10.1016/j.neunet.2007.04.016 · Cited by 948
Cited by
Recent advances in physical reservoir computing: A review
Tanaka, G., Yamane, T., Héroux, J. B., Nakane, R., Kanazawa, N., Takeda, S., Numata, H., Nakano, D., & Hirose, A. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.03.005 · Cited by 2,146
Neuromorphic photonic networks using silicon photonic weight banks
Tait, A. N., de Lima, T. F., Zhou, E., Wu, A. X., Nahmias, M. A., Shastri, B. J., & Prucnal, P. R. (2017) · Scientific Reports · doi:10.1038/s41598-017-07754-z · Cited by 849
The rise of intelligent matter
Kaspar, C., Ravoo, B. J., van der Wiel, W. G., Wegner, S. V., & Pernice, W. H. P. (2021) · Nature · doi:10.1038/s41586-021-03453-y · Cited by 526
Reprogrammable Electro-Optic Nonlinear Activation Functions for Optical Neural Networks
Williamson, I. A. D., Hughes, T. W., Minkov, M., Bartlett, B., Pai, S., & Fan, S. (2020) · IEEE Journal of Selected Topics in Quantum Electronics · doi:10.1109/jstqe.2019.2930455 · Cited by 346
MERRC: A Memristor-Enabled Reconfigurable Low-Power Reservoir Computing Architecture at the Edge
Nowshin, F., Huang, Y., Sarkar, M. R., Xia, Q., & Yi, Y. (2024) · IEEE Transactions on Circuits and Systems I: Regular Papers · doi:10.1109/tcsi.2023.3329337 · Cited by 12
Metadata
- Authors
- Duport, F., Smerieri, A., Akrout, A., Haelterman, M., & Massar, S.
- Venue
- Scientific Reports
- Published
- 2016-03-03
- Volume
- 6
- Issue
- 1
- Pages
- 22381-22381
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2317647934
- Cited by
- 206
- References
- 4
- FWCI
- 20.75
- Type
- journal article
- Language
- en
- Open access
- gold
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