The DOI matches its Crossref record; 99 of the 99 references it cites were found in a registry or an index.
Artificial neural networks enabled by nanophotonics
Open accessAbstract
The growing demands of brain science and artificial intelligence create an urgent need for the development of artificial neural networks (ANNs) that can mimic the structural, functional and biological features of human neural networks. Nanophotonics, which is the study of the behaviour of light and the light–matter interaction at the nanometre scale, has unveiled new phenomena and led to new applications beyond the diffraction limit of light. These emerging nanophotonic devices have enabled scientists to develop paradigm shifts of research into ANNs. In the present review, we summarise the recent progress in nanophotonics for emulating the structural, functional and biological features of ANNs, directly or indirectly.
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Cites
Geim, A. K., & Novoselov, K. S. (2007) · Nature Materials · doi:10.1038/nmat1849 · Cited by 39,979
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
Strukov, D. B., Snider, G. S., Stewart, D. R., & Williams, R. S. (2008) · Nature · doi:10.1038/nature06932 · Cited by 11,828
AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages
Cox, R. W. (1996) · Computers and Biomedical Research · doi:10.1006/cbmr.1996.0014 · Cited by 10,913
Memristor—The Missing Circuit Element
Chua, L. (1971) · IEEE Transactions on Circuit Theory · doi:10.1109/tct.1971.1083337 · Cited by 10,268 · cited 1×
“Memristors, the fourth basic element in electronic circuits predicted in 1971 31 and demonstrated in 2008 32 , provide a new path to demonstrate ANNs with a high integration of electronic devices.”
BackgroundImaging Intracellular Fluorescent Proteins at Nanometer Resolution
Betzig, E., Patterson, G. H., Sougrat, R., Lindwasser, O. W., Olenych, S., Bonifacino, J. S., Davidson, M. W., Lippincott-Schwartz, J., & Hess, H. F. (2006) · Science · doi:10.1126/science.1127344 · Cited by 8,772
Hell, S. W., & Wichmann, J. (1994) · Optics Letters · doi:10.1364/ol.19.000780 · Cited by 6,065
Nanoscale Memristor Device as Synapse in Neuromorphic Systems
Jo, S. H., Chang, T., Ebong, I., Bhadviya, B. B., Mazumder, P., & Lu, W. (2010) · Nano Letters · doi:10.1021/nl904092h · Cited by 4,174 · cited 1×
“…14 , light sources (LED and laser) 12 , optical ampli fi ers 52 , saturable absorbers 52 , optical bistable devices 53 Synapse Weighting Hologram 12 , MZI 15 , micro-ring resonators 14 Memory Electronic memories 12 – 15 , reversible optical memories 54 with nanoscale silicon-based memristors 34 .”
BackgroundA million spiking-neuron integrated circuit with a scalable communication network and interface
Merolla, P. A., Arthur, J. V., Alvarez-Icaza, R., Cassidy, A. S., Sawada, J., Akopyan, F., Jackson, B. L., Imam, N., Guo, C., Nakamura, Y., et al. (2014) · Science · doi:10.1126/science.1254642 · Cited by 4,147
Research works printed but not linked: 14
- Zhang, K. W. et al. A fiber optic sensor for the measurement of surface roughness and displacement using artificial neural networks. IEEE Trans. Instrum. Meas. 46, 899–902 (1997). · doi:10.1109/19.650796
- Uhrig, R. E. Introduction to artificial neural networks. Proceedings of IECON ‘95 - 21st Annual Conference on IEEE Industrial Electronics. Orlando, FL, USA: IEEE, pp 33–37 (1995).
- Hinton, G. Mental simulation. Nature 347, 627–628 (1990). · doi:10.1038/347627a0
- Hebb, D. O. The organization of behavior. in Neurocomputing: Foundations of Research (eds. Anderson, J. A. & Rosenfeld, E.) 43–54 (Cambridge, MA, USA: MIT Press, 1988).
- Deng, R. R. & Liu, X. G. Optical multiplexing: Tunable lifetime nanocrystals. Nat. Photonics 8, 10–12 (2014). · doi:10.1038/nphoton.2013.353
- Li, S. H. & Cai, X. H. High-contrast all optical bistable switching in coupled nonlinear photonic crystal microcavities. Appl. Phys. Lett. 96, 131114 (2010). · doi:10.1063/1.3378812
- Nicoletti, E. et al. Generation of λ/12 nanowires in chalcogenide glasses. Nano Lett. 11, 4218–4221 (2011). · doi:10.1021/nl202173t
- Li, X. P. et al. Light-control-light nanoplasmonic modulator for 3D micro-optical beam shaping. Adv. Opt. Mater. 4, 70–75 (2016). · DOI
- Környei, Z. et al. Cell sorting in a Petri dish controlled by computer vision. Sci. Rep. 3, 1088 (2013). · doi:10.1038/srep01088
- Yu, H. Y., Zhang, Q. M. & Gu, M. Three-dimensional direct laser writing of biomimetic neuron structures. Opt. Express 26, 32111–32117 (2018). · doi:10.1364/oe.26.032111
- Yu, H. Y. et al. Three-dimensional direct laser writing of neuron-inspired structures. Proceedings of the Frontiers in Optics 2017. FTu5D.2. Washington, DC, United States: Optical Society of America, 2017. · doi:10.1364/fio.2017.ftu5d.2
- Yu, H. Y., Zhang, Q. M. & Gu, M. Three-dimensional direct laser writing of ultra-low density neuron-inspired steiner tree structures. Proceedings of the Frontiers in Optics 2018. FM3D.2. Washington, DC, United States: Optical Society of America, 2018. · doi:10.1364/fio.2018.fm3d.2
- Zeeman, P. On the influence of magnetism on the nature of the light emitted by a substance. Astrophys. J. 5, 332 (1897). · doi:10.1086/140355
- Bradac, C. et al. Effect of the nanodiamond host on a nitrogen-vacancy color-centre emission state. Small 9, 132–139 (2013). · doi:10.1002/smll.201200574
Cited by
Deep learning enabled inverse design in nanophotonics
So, S., Badloe, T., Noh, J., Rho, J., & Bravo‐Abad, J. (2020) · Nanophotonics · doi:10.1515/nanoph-2019-0474 · Cited by 564
Programmable surface plasmonic neural networks for microwave detection and processing
Gao, X., Ma, Q., Gu, Z., Cui, W. Y., Liu, C., Zhang, J., & Cui, T. J. (2023) · Nature Electronics · doi:10.1038/s41928-023-00951-x · Cited by 106
Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey
Putrada, A. G., Abdurohman, M., Perdana, D., & Nuha, H. H. (2022) · IEEE Access · doi:10.1109/access.2022.3169765 · Cited by 101
“ANN emulates the structural, biological, and functional features of a biological neural network (BNN) [178].”
BackgroundSalgado, E. M., Esteves, A. F., Gonçalves, A. L., & Pires, J. C. M. (2023) · Environmental Research · doi:10.1016/j.envres.2023.116076 · Cited by 63
Evolution of epitaxial BaTiO3 on SrTiO3-buffered Si: Phase field analysis
Li, W., Landis, C. M., & Demkov, A. A. (2022) · Journal of Applied Physics · doi:10.1063/5.0111188 · Cited by 8
Metadata
- Authors
- Zhang, Q., Yu, H., Barbiero, M., Wang, B., & Gu, M.
- Venue
- Light: Science & Applications
- Published
- 2019-05-08
- Volume
- 8
- Issue
- 1
- Pages
- 42-42
- ISSN
- 2047-7538 (ISSN-L)
- OpenAlex
- W2944548960
- Cited by
- 317
- References
- 99
- FWCI
- 17.50
- Type
- journal article
- Language
- en
- Open access
- gold
- Licence
- creativecommons.org/licenses/by/4.0 (tdm)
- creativecommons.org/licenses/by/4.0 (vor)
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- Zhang, Q., Yu, H., Barbiero, M., Wang, B., & Gu, M. (2019). Artificial neural networks enabled by nanophotonics. Light: Science & Applications. https://doi.org/10.1038/s41377-019-0151-0
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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