On the Role of Astroglial Syncytia in Self-Repairing Spiking Neural Networks
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 1269
- Type
- D - Journal article
- DOI
-
10.1109/TNNLS.2014.2382334
- Title of journal
- IEEE Transactions on Neural Networks and Learning Systems
- Article number
- -
- First page
- 2370
- Volume
- 26
- Issue
- 10
- ISSN
- 1045-9227
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- Year of publication
- 2015
- 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
-
4
- Research group(s)
-
D - RCEEE
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work is a key output of an EPSRC grant (£40k, 2014, EFXD12011), through the eFutures programme, jointly with Ulster University. The originality in this work is a computer model of how cells in the brain (astrocytes and neurons) communicate to bring about a localised self-repairing capability. The model produced results for the first time on the concept of brain-like self-repair to develop the first truly brain-inspired computational machine. The paper represented a disruptive approach to the current thinking about self-repairing computational systems. This work helped secure EPSRC funding (£1.06m, Oct/2015-Oct/2019, EP/N00714X/1&EP/N007050/1) for the project ‘Self-repairing hardware paradigms’.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -