Tunable superconducting neurons for networks based on radial basis functions
Conţinutul numărului revistei
Articolul precedent
Articolul urmator
102 7
Ultima descărcare din IBN:
2022-11-12 01:04
SM ISO690:2012
SCHEGOLEV, Andrey; KLENOV, Nikolai V.; BAKURSKIY, Sergey V.; SOLOVIEV, Igor I.; KUPRIYANOV, Mihail; TERESHONOK , Maxim; SIDORENKO, Anatolie. Tunable superconducting neurons for networks based on radial basis functions. In: Beilstein Journal of Nanotechnology. 2022, nr. 13, pp. 444-454. ISSN 2190-4286.
EXPORT metadate:
Google Scholar

Dublin Core
Beilstein Journal of Nanotechnology
Numărul 13 / 2022 / ISSN 2190-4286

Tunable superconducting neurons for networks based on radial basis functions

DOI: https://doi.org/10.3762/bjnano.13.37

Pag. 444-454

Schegolev Andrey12, Klenov Nikolai V.34, Bakurskiy Sergey V.1, Soloviev Igor I.1, Kupriyanov Mihail1, Tereshonok Maxim2, Sidorenko Anatolie56
1 D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University,
2 Moscow Technical University of Communications and Informatics,
3 Lomonosov Moscow State University,
4 Lobachevsky State University of Nizhni Novgorod,
5 Institute of the Electronic Engineering and Nanotechnologies "D. Ghitu",
6 Orel State University
Disponibil în IBN: 11 iulie 2022


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.

networks on radial basis functions, Josephson circuits, radial basis functions (RBFs), spintronics, superconducting electronics, Superconducting neural network