A Supervised Learning Algorithm for Learning Precise Timing of Multiple Spikes in Multilayer Spiking Neural Networks
- Submitting institution
-
University of Northumbria at Newcastle
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 22063358
- Type
- D - Journal article
- DOI
-
10.1109/TNNLS.2018.2797801
- Title of journal
- IEEE Transactions on Neural Networks and Learning Systems
- Article number
- -
- First page
- 5394
- Volume
- 29
- Issue
- 11
- ISSN
- 2162-237X
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2018
- 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
-
3
- Research group(s)
-
E - Intelligent Systems Research Group (iSRG)
- Citation count
- 18
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This article, featured in the top (13/47/31) most popular IEEE Transactions on Neural Networks and Learning Systems articles in Nov2018/Dec2018/Jan2019, respectively (cis.ieee.org/publications/t-neural-networks-and-learning-systems), pioneers a bio-inspired supervised learning approach for training spiking-neurons to fire temporally-precise spike-patterns in response to a given class of temporally-encoded input patterns.
This research has significant implications for neuroprosthetics/neuroengineering and led to further research collaboration with the Computational Intelligence Laboratory, University of Electronic Science and Technology of China, Chengdu. Belatreche was invited to deliver talks in academic meetings including a keynote speech on the bio-inspired computational aspects of this article at IEEE-PAIS (24-25/10/2018, Tebessa_Algeria https://pais2018.sciencesconf.org.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -