Evolving spiking networks with variable resistive memories
- Submitting institution
-
University of the West of England, Bristol
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 824704
- Type
- D - Journal article
- DOI
-
10.1162/EVCO_a_00103
- Title of journal
- Evolutionary Computation
- Article number
- -
- First page
- 79
- Volume
- 22
- Issue
- 1
- ISSN
- 1063-6560
- Open access status
- Out of scope for open access requirements
- Month of publication
- February
- Year of publication
- 2014
- URL
-
http://dx.doi.org/10.1162/EVCO_a_00103
- 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
-
4
- Research group(s)
-
-
- Citation count
- 14
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper describes the first known use of heterogeneous memristive synapse within spiking neural networks, designed used evolutionary computing techniques. The approach is compared against a set of resistive memory synapse types and how the extra degrees of temporally dynamic behaviour is exploited is investigated. The work was funded by an EPSRC grant and the Unit staff’s co-authors are chemists who contributed to considering the physical properties and realisation of such devices in particular. The paper made the ACM Computing Reviews 19th Annual Best of Computing Notable Books and Articles list in 2014.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
