Cortical microcircuits as gated-recurrent neural networks
- Submitting institution
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University of Bristol
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 166154329
- Type
- E - Conference contribution
- DOI
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- Title of conference / published proceedings
- Neural Information Processing Systems 30 (NIPS 2017) : Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4-9 December 2017, Long Beach, CA, USA.
- First page
- 272
- Volume
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- Issue
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- ISSN
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- Open access status
- -
- Month of publication
- November
- Year of publication
- 2017
- URL
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- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
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- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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4
- Research group(s)
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A - Artificial Intelligence and Autonomy
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- As referenced, this paper "opened a venue for interdisciplinary dialog between questions of brain function and theoretical insights from deep learning" (Tavanaei et al https://doi.org/10.1016/j.neunet.2018.12.002), helped the first author secure a permanent lecturer position and be recognised as leading researcher in the field (e.g., a co-author of Blake et al. NatureNeuroscience 2019). It was highlighted by MIT Tech Review as "the most thought-provoking papers from the Physics arXiv" for two consecutive weeks (https://www.technologyreview.com/s/609463/the-best-of-the-physics-arxiv-week-ending-november-11-2017/). Multiple invited seminars by the authors (e.g. at University of Sheffield and ETH-Zurich). Started research by others [https://link.springer.com/chapter/10.1007/978-3-030-01418-6_28)] and a new PhD student working on the topic.
- Author contribution statement
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- Non-English
- No
- English abstract
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