Fully memristive neural networks for pattern classification with unsupervised learning
- Submitting institution
-
Loughborough University
- Unit of assessment
- 9 - Physics
- Output identifier
- 840
- Type
- D - Journal article
- DOI
-
10.1038/s41928-018-0023-2
- Title of journal
- Nature Electronics
- Article number
- -
- First page
- 137
- Volume
- 1
- Issue
- 2
- ISSN
- 2520-1131
- Open access status
- Compliant
- Month of publication
- February
- Year of publication
- 2018
- URL
-
-
- 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
-
23
- Research group(s)
-
-
- Citation count
- 278
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- -
- Author contribution statement
- As the only theoretician in the list of the co-authors, Savel'ev developed the model describing firing of an artificial neuron comprising of a diffusive memristor and a capacitor. He performed all simulations and led data analisys. In addition, he analysed the nanoparticle redox influence on the artificial neuron. The model is described in the method session and supplementary materials. His simulations are shown in Fig. 1, S1, S4, and S12. Savel’ev presented results as an invited talk at “New Trends in Nonequilibrium Statistical Mechanics: Classical and Quantum Systems” conference, Erice (2018).
- Non-English
- No
- English abstract
- -