Unsupervised learning in an ensemble of spiking neural networks mediated by ITDP
- Submitting institution
-
University of Sussex
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1334_64939
- Type
- D - Journal article
- DOI
-
10.1371/journal.pcbi.1005137
- Title of journal
- PLoS Computational Biology
- Article number
- e1005137
- First page
- -
- Volume
- 12
- Issue
- 10
- ISSN
- 1553-734X
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2016
- URL
-
https://doi.org/10.1371/journal.pcbi.1005137
- 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
- Yes
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 8
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "Interdisciplinary work involving collaboration between computational scientists and leading neuroscientists. Provides first example of ensemble learning in spiking networks, along with first functioning model of ITDP. Involves a rigorous comparison between alternative architectures. This work is having impact in both experimental and computational neuroscience as evidenced by citations in leading journals (e.g. [1]) and has led to a new programme of work in modelling biological ensemble learning. PLoS Computational Biology is the premier high impact specialist international outlet for research in computational biology. It has a high rejection rate.?
https://doi.org/10.1038/s41593-018-0263-5"
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -