SpikeTemp: an enhanced rank-order-based learning approach for spiking neural networks with adaptive structure
- Submitting institution
-
Nottingham Trent University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 31 - 697358
- Type
- D - Journal article
- DOI
-
10.1109/TNNLS.2015.2501322
- Title of journal
- IEEE Transactions on Neural Networks and Learning Systems
- Article number
- -
- First page
- 30
- Volume
- 28
- Issue
- 1
- ISSN
- 2162-237X
- Open access status
- Out of scope for open access requirements
- Month of publication
- December
- 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
-
3
- Research group(s)
-
A - Computing and Informatics Research Centre
- Citation count
- 20
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The lack of high-performance learning algorithms in the spiking neural network (SNN) field is a major limitation, which this paper addresses.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
