Artificial Epigenetic Networks: Automatic Decomposition of Dynamical Control Tasks Using Topological Self-Modification
- Submitting institution
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University of Nottingham, The
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 5204657
- Type
- D - Journal article
- DOI
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10.1109/tnnls.2015.2497142
- Title of journal
- IEEE Transactions on Neural Networks and Learning Systems
- Article number
- -
- First page
- 218
- Volume
- 28
- Issue
- 1
- ISSN
- 2162-2388
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- Year of publication
- 2016
- 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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4
- Research group(s)
-
-
- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Connectionist architectures are often “black boxes” and their decision-making process is difficult to understand. This paper describes a computational technique that allows biologically inspired connectionist architectures to provide a rationale for their decision-making process. Additionally, such techniques can also yield performance increases when applied to certain tasks. This work appeared in the leading journal dedicated to neural networks and associated learning systems formed the backbone of the EPSRC grant “Using Epigenetically-Inspired Connectionist Models to Provide Transparency in the Modelling of Human Visceral Leismaniasis” (EP/S003207/1).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -