Homeostatic Fault Tolerance in Spiking Neural Networks: A Dynamic Hardware Perspective
- Submitting institution
-
University of Ulster
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 76610403
- Type
- D - Journal article
- DOI
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10.1109/TCSI.2017.2726763
- Title of journal
- IEEE Transactions on Circuits and Systems I: Regular Papers
- Article number
- -
- First page
- 687
- Volume
- 65
- Issue
- 2
- ISSN
- 1549-8328
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- 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
-
8
- Research group(s)
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A - Intelligent Systems Research Centre
- Citation count
- 27
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- <01> This research is supported by EPSRC funding (EP/N007050/1) and in collaboration with Univ. of York and resulted in 6 additional papers (10.1109/VLSID.2018.36; https://doi.org/10.1007/978-3-319-70136-3_41; 10.1109/VLSID.2018.36; 10.1109/NANO.2018.8626301; https://doi.org/10.1007/978-3-030-30487-4_57; 10.23919/DATE48585.2020.9116312; 10.1109/TNNLS.2018.2854291). First author (Johnson) was appointed as the Senior Lecturer at University of Huddersfield (2019) and third author (Alan Millard) appointed Lecturer at the University of Lincoln (2020). Follow up bid to EPSRC was submitted (EP/W003783/1). Fourth author (Shvan Karim) obtained his PhD (2020) and secured employment as a Digital Design Engineer with Magics Instruments (Belgium) in 2020.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -