Optimization of output spike train encoding for a spiking neuron based on its spatio-temporal input pattern
- Submitting institution
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Loughborough University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1974
- Type
- D - Journal article
- DOI
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10.1109/TCDS.2019.2909355
- Title of journal
- IEEE Transactions on Cognitive and Developmental Systems
- Article number
- 3
- First page
- 427
- Volume
- 12
- Issue
- 3
- ISSN
- 2379-8920
- Open access status
- Technical exception
- Month of publication
- April
- Year of publication
- 2019
- 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
-
2
- Research group(s)
-
-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The work has attracted interest from Railston & Co Ltd with whom Dr Cosma is exploring Spiking Neural Networks (SNNs) for inspecting wind turbine blades using drone technologies. Given that SNNs are particularly efficient for computer vision systems, the proposed work is attracting interest from companies working on machine vision applications. Importantly, the paper adds to the limited body of literature on SNNs applied to classification tasks.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
