Deep learning and deep knowledge representation in Spiking Neural Networks for Brain-Computer Interfaces
- Submitting institution
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University of Ulster
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 85782437
- Type
- D - Journal article
- DOI
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10.1016/j.neunet.2019.08.029
- Title of journal
- Neural Networks
- Article number
- -
- First page
- 169
- Volume
- 121
- Issue
- -
- ISSN
- 0893-6080
- Open access status
- Deposit exception
- Month of publication
- September
- Year of publication
- 2019
- URL
-
-
- Supplementary information
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- 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
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2
- Research group(s)
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A - Intelligent Systems Research Centre
- Citation count
- 7
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- <20> This is the world first paper to extract symbolic, spatio-temporal rules from a brain-inspired spiking neural network (SNN), trained on EEG data from subjects conducting mental or physical tasks. The work was part of a funded project (SRIF/AUT/Intellect, 450,000NZD; 2015-2019; PI N.Kasabov) and led to new results published in Scientific Reports (https://doi.org/10.1038/s41598-021-81805-4) and was presented at IJCNN2019 conference. Kumarasinghe completed successfully PhD study in 2021 and joined SparkNZ
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -