The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
High-performance photonic reservoir computer based on a coherently driven passive cavity
Open accessAbstract
Reservoir computing is a recent bio-inspired approach for processing time-dependent signals. It has enabled a breakthrough in analog information processing, with several experiments, both electronic and optical, demonstrating state-of-the-art performances for hard tasks such as speech recognition, time series prediction and nonlinear channel equalization. A proof-of-principle experiment using a linear optical circuit on a photonic chip to process digital signals was recently reported. Here we present a photonic implementation of a reservoir computer based on a coherently driven passive fiber cavity processing analog signals. Our experiment has error rate as low or lower than previous experiments on a wide variety of tasks, and also has lower power consumption. Furthermore, the analytical model describing our experiment is also of interest, as it constitutes a very simple high performance reservoir computer algorithm. The present experiment, given its good performances, low energy consumption and conceptual simplicity, confirms the great potential of photonic reservoir computing for information processing applications ranging from artificial intelligence to telecommunications
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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
Information processing using a single dynamical node as complex system
Appeltant, L., Soriano, M. C., Van der Sande, G., Danckaert, J., Massar, S., Dambre, J., Schrauwen, B., Mirasso, C. R., & Fischer, I. (2011) · Nature Communications · doi:10.1038/ncomms1476 · Cited by 1,716
Self-citationParallel photonic information processing at gigabyte per second data rates using transient states
Brunner, D., Soriano, M. C., Mirasso, C. R., & Fischer, I. (2013) · Nature Communications · doi:10.1038/ncomms2368 · Cited by 938
Experimental demonstration of reservoir computing on a silicon photonics chip
Vandoorne, K., Mechet, P., Van Vaerenbergh, T., Fiers, M., Morthier, G., Verstraeten, D., Schrauwen, B., Dambre, J., & Bienstman, P. (2014) · Nature Communications · doi:10.1038/ncomms4541 · Cited by 892
Self-citationLarger, L., Soriano, M. C., Brunner, D., Appeltant, L., Gutierrez, J. M., Pesquera, L., Mirasso, C. R., & Fischer, I. (2012) · Optics Express · doi:10.1364/oe.20.003241 · Cited by 864
Optoelectronic Reservoir Computing
Paquot, Y., Duport, F., Smerieri, A., Dambre, J., Schrauwen, B., Haelterman, M., & Massar, S. (2012) · Scientific Reports · doi:10.1038/srep00287 · Cited by 783
Self-citationAll-optical reservoir computing
Duport, F., Schneider, B., Smerieri, A., Haelterman, M., & Massar, S. (2012) · Optics Express · doi:10.1364/oe.20.022783 · Cited by 502
Self-citationInformation Processing Capacity of Dynamical Systems
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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
All-optical spiking neurosynaptic networks with self-learning capabilities
Feldmann, J., Youngblood, N., Wright, C. D., Bhaskaran, H., & Pernice, W. H. P. (2019) · Nature · doi:10.1038/s41586-019-1157-8 · Cited by 1,529
Scalable reservoir computing on coherent linear photonic processor
Nakajima, M., Tanaka, K., & Hashimoto, T. (2021) · Communications Physics · doi:10.1038/s42005-021-00519-1 · Cited by 203
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
- Vinckier, Q., Duport, F., Smerieri, A., Vandoorne, K., Bienstman, P., Haelterman, M., & Massar, S.
- Venue
- Optica
- Publisher
- Optica Publishing Group
- Published
- 2015-04-29
- Volume
- 2
- Issue
- 5
- Pages
- 438
- ISSN
- 2334-2536 (ISSN-L)
- OpenAlex
- W2170993585
- Cited by
- 265
- References
- 33
- FWCI
- 17.83
- Type
- journal article
- Language
- en
- Open access
- gold
- Licence
- doi.org/10.1364/OA_License_v1#VOR-OA (vor)
- opg.optica.org/policies/opg-tdm-policy.json (tdm)
- Funders
- Action de Recherche Concerte (ARC) (AUWB-2012-12/17-ULB9)
- Interuniversity Attraction Poles (IAP) program of the Belgian Science Policy Office (BELSPO) (IAP P7-35 photonics@be)
- European Research Council (ERC) (Naresco (ref. 239599))
- Fonds de la Recherche Scientifique FRS-FNRS (FRFC: T.0092.14)
- Fonds pour la formation la Recherche dans lIndustrie et dans lAgriculture (FRIA)
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- Vinckier, Q., Duport, F., Smerieri, A., Vandoorne, K., Bienstman, P., Haelterman, M., & Massar, S. (2015). High-performance photonic reservoir computer based on a coherently driven passive cavity. Optica. https://doi.org/10.1364/optica.2.000438
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