Photonic computing devices are a compelling alternative to conventional computing setups for machine learning applications, as they are nonlinear, fast and easy to parallelize. Recent work demonstrates the potential of these optical systems to process and classify human motion from video.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Antonik, P., Marsal, N., Brunner, D. & Rontani, D. Nat. Mach. Intell. https://doi.org/10.1038/s42256-019-0110-8 (2019).
Jaeger, H. The “Echo State” Approach to Analysing and Training Recurrent Neural Networks (GMD, 2001).
Maass, W., Natschläger, T. & Markram, H. Neural Comp. 14, 2531–2560 (2002).
Fernando, C. & Sojakka, S. in Advances in Artificial Life 588–597 (Springer, 2003).
Dockendorf, K. P., Park, I., He, P., Príncipe, J. C. & DeMarse, T. B. Biosystems 95, 90–97 (2009).
Bueno, J. et al. Optica 5, 756–760 (2018).
Van der Sande, G., Brunner, D. & Soriano, M. C. Nanophotonics 6, 561 (2017).
Shastri, B. J. et al. in Encyclopedia of Complexity and Systems Science (ed. Meyers, R. A.) 702 (Springer, 2018).
Shen, Y. et al. Nat. Photon. 11, 441–446 (2017).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Lüdge, K., Röhm, A. Computing with a camera. Nat Mach Intell 1, 551–552 (2019). https://doi.org/10.1038/s42256-019-0124-2
Published:
Issue Date:
DOI: https://doi.org/10.1038/s42256-019-0124-2
