Artificial Epigenetic Networks : Automatic Decomposition of Dynamical Control Tasks using Topological Self-Modification
- Submitting institution
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University of York
- Unit of assessment
- 12 - Engineering
- Output identifier
- 55026113
- 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
- 7372471
- First page
- 218
- Volume
- 28
- Issue
- 1
- ISSN
- 2162-237X
- 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
- Yes
- Number of additional authors
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4
- Research group(s)
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B - Intelligent Systems and Nano-Science
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper on bio-inspired models for novel computational systems is the first to use chromatin modification within genetic regulatory networks to create self-modification in recurrent neural networks, embedding the ability to express more general solutions. This work was developed with funding from the EPSRC Albino project (EP/F060041/1, £613,292) and provided foundations for the EPSRC Platform Grant (EP/K040820/1, £919,337). The fundamental work on understanding epigenetic mechanisms is a core idea in the EPSRC Programme Grant 'Re-Imagining Engineering Design: Growing Radical Cyber-Physical-Socio Phenotypes' (EP/V007335/1, £1,551,813).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -