Open Access
Beilstein Journal of Nanotechnology, volume 13, pages 444-454
Tunable superconducting neurons for networks based on radial basis functions
Andrey E. Schegolev
1, 2
,
Nikolay V. Klenov
1, 3
,
Sergey V Bakurskiy
1, 4
,
Igor I. Soloviev
1
,
M.V. Tereshonok
2
,
1
Lomonosov Moscow State University, Faculty of Physics
|
2
Moscow Technical University of Communications and Informatics
|
Publication type: Journal Article
Publication date: 2022-05-18
Journal:
Beilstein Journal of Nanotechnology
Q2
Q2
SJR: 0.520
CiteScore: 5.7
Impact factor: 2.6
ISSN: 21904286
General Physics and Astronomy
General Materials Science
Electrical and Electronic Engineering
Abstract
The hardware implementation of signal microprocessors based on superconducting technologies seems relevant for a number of niche tasks where performance and energy efficiency are critically important. In this paper, we consider the basic elements for superconducting neural networks on radial basis functions. We examine the static and dynamic activation functions of the proposed neuron. Special attention is paid to tuning the activation functions to a Gaussian form with relatively large amplitude. For the practical implementation of the required tunability, we proposed and investigated heterostructures designed for the implementation of adjustable inductors that consist of superconducting, ferromagnetic, and normal layers.
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GOST
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Schegolev A. E. et al. Tunable superconducting neurons for networks based on radial basis functions // Beilstein Journal of Nanotechnology. 2022. Vol. 13. pp. 444-454.
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Schegolev A. E., Klenov N. V., Bakurskiy S. V., Soloviev I. I., Kupriyanov M. Y., Tereshonok M., Sidorenko A. S. Tunable superconducting neurons for networks based on radial basis functions // Beilstein Journal of Nanotechnology. 2022. Vol. 13. pp. 444-454.
Cite this
RIS
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TY - JOUR
DO - 10.3762/bjnano.13.37
UR - https://doi.org/10.3762/bjnano.13.37
TI - Tunable superconducting neurons for networks based on radial basis functions
T2 - Beilstein Journal of Nanotechnology
AU - Schegolev, Andrey E.
AU - Klenov, Nikolay V.
AU - Bakurskiy, Sergey V
AU - Soloviev, Igor I.
AU - Kupriyanov, Mikhail Yu.
AU - Tereshonok, M.V.
AU - Sidorenko, Anatoli S
PY - 2022
DA - 2022/05/18
PB - Beilstein-Institut
SP - 444-454
VL - 13
SN - 2190-4286
ER -
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BibTex (up to 50 authors)
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@article{2022_Schegolev,
author = {Andrey E. Schegolev and Nikolay V. Klenov and Sergey V Bakurskiy and Igor I. Soloviev and Mikhail Yu. Kupriyanov and M.V. Tereshonok and Anatoli S Sidorenko},
title = {Tunable superconducting neurons for networks based on radial basis functions},
journal = {Beilstein Journal of Nanotechnology},
year = {2022},
volume = {13},
publisher = {Beilstein-Institut},
month = {may},
url = {https://doi.org/10.3762/bjnano.13.37},
pages = {444--454},
doi = {10.3762/bjnano.13.37}
}