All-optical spiking neurosynaptic networks with self-learning capabilities
- Submitting institution
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University of Exeter
- Unit of assessment
- 12 - Engineering
- Output identifier
- 5121
- Type
- D - Journal article
- DOI
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10.1038/s41586-019-1157-8
- Title of journal
- Nature
- Article number
- -
- First page
- 208
- Volume
- 569
- Issue
- 7755
- ISSN
- 0028-0836
- Open access status
- Compliant
- Month of publication
- May
- Year of publication
- 2019
- URL
-
-
- Supplementary information
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https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-019-1157-8/MediaObjects/41586_2019_1157_MOESM1_ESM.pdf
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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4
- Research group(s)
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E - Nano Engineering Science and Technology
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- First ever demonstration of self-learning, fully-scalable, integrated photonic spiking neural network. Carried out in collaboration with Muenster and Oxford, as part of H2020 Fun-COMP project led by Wright. Highlighted by Nature itself in News & Views article "A role for optics in AI Hardware", 8/5/2019. Generated high-profile publicity around world, including features in Le Scienze, Nanowerk, Innovations Report, Psychology Today, Photonics Views, Science Daily. Led to filing of patent (Wright co-inventor) in 2020 for a "photonic in-memory co-processor" with IBM Zurich (US patent app P201904211US01). Led to 2020 5Euro million EU FET Proactive grant award PHOENICS (with IBM, Muenster, EPFL).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -