The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Optoelectronic Reservoir Computing
Open accessAbstract
Reservoir computing is a recently introduced, highly efficient bio-inspired approach for processing time dependent data. The basic scheme of reservoir computing consists of a non linear recurrent dynamical system coupled to a single input layer and a single output layer. Within these constraints many implementations are possible. Here we report an optoelectronic implementation of reservoir computing based on a recently proposed architecture consisting of a single non linear node and a delay line. Our implementation is sufficiently fast for real time information processing. We illustrate its performance on tasks of practical importance such as nonlinear channel equalization and speech recognition, and obtain results comparable to state of the art digital implementations.
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Cites
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
Deep learning with coherent nanophotonic circuits
Shen, Y., Harris, N. C., Skirlo, S., Prabhu, M., Baehr-Jones, T., Hochberg, M., Sun, X., Zhao, S., Larochelle, H., Englund, D., et al. (2017) · Nature Photonics · doi:10.1038/nphoton.2017.93 · Cited by 3,340
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
Photonics for artificial intelligence and neuromorphic computing
Shastri, B. J., Tait, A. N., Ferreira de Lima, T., Pernice, W. H. P., Bhaskaran, H., Wright, C. D., & Prucnal, P. R. (2021) · Nature Photonics · doi:10.1038/s41566-020-00754-y · Cited by 1,756
Parallel 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
Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing
(2021) · doi:10.1038/s41467-020-20692-1 · Cited by 597
Metadata
- Authors
- Paquot, Y., Duport, F., Smerieri, A., Dambre, J., Schrauwen, B., Haelterman, M., & Massar, S.
- Venue
- Scientific Reports
- Published
- 2012-02-27
- Volume
- 2
- Issue
- 1
- Pages
- 287-287
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2113132555
- Cited by
- 783
- References
- 3
- FWCI
- 24.06
- Type
- journal article
- Language
- en
- Open access
- gold
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- Paquot, Y., Duport, F., Smerieri, A., Dambre, J., Schrauwen, B., Haelterman, M., & Massar, S. (2012). Optoelectronic Reservoir Computing. Scientific Reports. https://doi.org/10.1038/srep00287
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