Learning and encoding motor primitives for limb actions in a brain-like computation approach
- Submitting institution
-
Brunel University London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 087-214195-14958
- Type
- D - Journal article
- DOI
-
10.1016/j.neucom.2019.12.051
- Title of journal
- Neurocomputing
- Article number
- -
- First page
- 160
- Volume
- 385
- Issue
- -
- ISSN
- 0925-2312
- Open access status
- Deposit exception
- Month of publication
- December
- Year of publication
- 2019
- URL
-
http://bura.brunel.ac.uk/handle/2438/20955
- 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)
-
1 - Artificial Intelligence (AI)
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Guided by the dynamical system theory of motor coding, an innovative Recurrent Neural Network based computational model is investigated that has generated automatic behaviour similar to what has been found in the electrophysiological studies. This study is therefore not just a proof of the findings from neuroscience, but also an indication of the promising use of computer science in simulating brain activities to solve real world problems such as automatic control in Industry 4.0, wheelchair and healthcare. In particular, the possible application of the work to wheelchair robots is currently in discussion with industrial partners.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
