Simulating self-learning in photorefractive optical reservoir computers
- Author
- Floris Laporte, Joni Dambre (UGent) and Peter Bienstman (UGent)
- Organization
- Project
- Abstract
- Photorefractive materials exhibit an interesting plasticity under the influence of an optical field. By extending the finite-difference time-domain method to include the photorefractive effect, we explore how this property can be exploited in the context of neuromorphic computing for telecom applications. By first priming the photorefractive material with a random bit stream, the material reorganizes itself to better recognize simple patterns in the stream. We demonstrate this by simulating a typical reservoir computing setup, which gets a significant performance boost on performing the XOR on two consecutive bits in the stream after this initial priming step.
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8738686
- MLA
- Laporte, Floris, et al. “Simulating Self-Learning in Photorefractive Optical Reservoir Computers.” SCIENTIFIC REPORTS, vol. 11, no. 1, 2021, doi:10.1038/s41598-021-81899-w.
- APA
- Laporte, F., Dambre, J., & Bienstman, P. (2021). Simulating self-learning in photorefractive optical reservoir computers. SCIENTIFIC REPORTS, 11(1). https://doi.org/10.1038/s41598-021-81899-w
- Chicago author-date
- Laporte, Floris, Joni Dambre, and Peter Bienstman. 2021. “Simulating Self-Learning in Photorefractive Optical Reservoir Computers.” SCIENTIFIC REPORTS 11 (1). https://doi.org/10.1038/s41598-021-81899-w.
- Chicago author-date (all authors)
- Laporte, Floris, Joni Dambre, and Peter Bienstman. 2021. “Simulating Self-Learning in Photorefractive Optical Reservoir Computers.” SCIENTIFIC REPORTS 11 (1). doi:10.1038/s41598-021-81899-w.
- Vancouver
- 1.Laporte F, Dambre J, Bienstman P. Simulating self-learning in photorefractive optical reservoir computers. SCIENTIFIC REPORTS. 2021;11(1).
- IEEE
- [1]F. Laporte, J. Dambre, and P. Bienstman, “Simulating self-learning in photorefractive optical reservoir computers,” SCIENTIFIC REPORTS, vol. 11, no. 1, 2021.
@article{8738686,
abstract = {{Photorefractive materials exhibit an interesting plasticity under the influence of an optical field. By extending the finite-difference time-domain method to include the photorefractive effect, we explore how this property can be exploited in the context of neuromorphic computing for telecom applications. By first priming the photorefractive material with a random bit stream, the material reorganizes itself to better recognize simple patterns in the stream. We demonstrate this by simulating a typical reservoir computing setup, which gets a significant performance boost on performing the XOR on two consecutive bits in the stream after this initial priming step.}},
articleno = {{2701}},
author = {{Laporte, Floris and Dambre, Joni and Bienstman, Peter}},
issn = {{2045-2322}},
journal = {{SCIENTIFIC REPORTS}},
language = {{eng}},
number = {{1}},
pages = {{10}},
title = {{Simulating self-learning in photorefractive optical reservoir computers}},
url = {{http://doi.org/10.1038/s41598-021-81899-w}},
volume = {{11}},
year = {{2021}},
}
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