An analytical comparison of locally-connected reconfigurable neural network architectures using a C. elegans locomotive model
- Submitting institution
-
The University of Bath
- Unit of assessment
- 12 - Engineering
- Output identifier
- 196622159
- Type
- D - Journal article
- DOI
-
10.3390/computers7030043
- Title of journal
- Computers
- Article number
- 43
- First page
- -
- Volume
- 7
- Issue
- 3
- ISSN
- 2073-431X
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
https://www.mdpi.com/2073-431X/7/3/43/s1
- 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)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper introduces new reconfigurable architectures for implementing biologically inspired neural networks in real-time. This research area went on to form a core component of the engineering of biologically inspired neural networks in real-time within the new EPSRC-funded Accountable, Responsible and Transparent AI (ART-AI) CDT (EPSRC EP/S023437/1), 3 new PhD studentships, and the building of a successful collaboration with the Royal Agricultural University for automatic identification of plants.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
