Exploring Self-Repair in a Coupled Spiking Astrocyte Neural Network
- Submitting institution
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The University of Huddersfield
- Unit of assessment
- 12 - Engineering
- Output identifier
- 89
- Type
- D - Journal article
- DOI
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10.1109/TNNLS.2018.2854291
- Title of journal
- IEEE Transactions on Neural Networks and Learning Systems
- Article number
- 8423789
- First page
- 865
- Volume
- 30
- Issue
- 3
- ISSN
- 2162-237X
- Open access status
- Technical exception
- Month of publication
- July
- Year of publication
- 2018
- URL
-
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- Supplementary information
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- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
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9
- Research group(s)
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-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The work is interdisciplinary research combining electronic engineering with neuroscience in developing brain-inspired systems for critical field including the defence. My contribution is the mapping of bio-inspired algorithms on to the electronic hardware. The work developed autonomous fault-tolerant robots and was demonstrated at the IEEE DATE 2018 conference. The work opened collaborations with the Department of Neuroscience, University of Minnesota, US and resulted in conducting successfully the first international workshop -SPANNER-2018. This work resulted in talks at IEEE academic conferences including VLSID-2018, SSCI 2018 and an invitation for organizing a deep-learning workshop to be conducted with NVIDIA at EMIT-2019.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -