Overview of Neuromorphic Optical Systems
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-48655-5_2
Reference25 articles.
1. Dupeyroux, Julien, Jesse J. Hagenaars, Federico Paredes-Vallés, and Guido CHE de Croon. 2021. Neuromorphic control for optic-flow-based landing of MAVs using the Loihi processor. In 2021 IEEE International Conference on Robotics and Automation (ICRA), 96–102. Xi’an, China. https://doi.org/10.1109/ICRA48506.2021.9560937
2. Bile, A., F. Moratti, H. Tari, et al. 2021. Supervised and unsupervised learning using a fully-plastic all-optical unit of artificial intelligence based on solitonic waveguides. Neural Computing and Applications 33: 17071–17079. https://doi.org/10.1007/s00521-021-06299-7.
3. Feldmann, J., N. Youngblood, C.D. Wright, et al. 2019. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature 569: 208–214. https://doi.org/10.1038/s41586-019-1157-8.
4. Tari, H., A. Bile, F. Moratti, et al. 2022. Sigmoid type neuromorphic activation function based on saturable absorption behavior of graphene/PMMA composite for intensity modulation of surface plasmon polariton signals. Plasmonics 17: 1025–1032. https://doi.org/10.1007/s11468-021-01553-z.
5. Mead, C. 1990. Neuromorphic electronic systems. Proceedings of the IEEE 78 (10): 1629–1636. https://doi.org/10.1109/5.58356.
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