Classification and regression of spatio-temporal signals using NeuCube and its realization on SpiNNaker neuromorphic hardware
- Submitting institution
-
The University of Manchester
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 118621595
- Type
- D - Journal article
- DOI
-
10.1088/1741-2552/aafabc
- Title of journal
- Journal of Neural Engineering
- Article number
- 026014
- First page
- -
- Volume
- 16
- Issue
- 2
- ISSN
- 1741-2560
- Open access status
- Exception within 3 months of publication
- Month of publication
- February
- 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
-
9
- Research group(s)
-
A - Computer Science
- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "The outcome of a collaboration between Auckland University of Technology, TU Munich and the University of Manchester, this paper describes work to use SpiNNaker for real time processing of raw surface electromyography and electroencephalography signals, promising to accelerate the development of next-generation low-power, portable and intelligent prosthetic hands.
The work demonstrates the potential of spiking neural networks and neuromorphic hardware to support the real-time processing of complex spatio-temporal signals, and was the primary output from a memorandum of understanding between the three institutions."
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
