Tunable superconducting neurons for networks based on radial basis functions

  1. Andrey E. Schegolev,
  2. Nikolay V. KlenovORCID Logo,
  3. Sergey V. Bakurskiy,
  4. Igor I. Soloviev,
  5. Mikhail Yu. KupriyanovORCID Logo,
  6. Maxim V. Tereshonok and
  7. Anatoli S. Sidorenko

Submitting author affiliation: Lomonosov Moscow State University, Moscow, Russian Federation

Beilstein Arch. 2022, 202216. https://doi.org/10.3762/bxiv.2022.16.v1

Published 17 Mar 2022

  • Preprint


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 (RBF). We examine the static and dynamic activation functions of the proposed neuron. Special attention is paid to tuning of the activation functions to the Gaussian form with relatively large amplitude. We proposed and investigated heterostructures designed for the implementation of tunable inductors which consist of superconducting, ferromagnetic, and normal layers.

Keywords: superconducting electronics; Josephson circuits; spintronics; superconducting neural network; networks on radial-basis functions

How to Cite

When a peer-reviewed version of this preprint is available, this information will be updated in the information box above. If no peer-reviewed version is available, please cite this preprint using the following information:

Schegolev, A. E.; Klenov, N. V.; Bakurskiy, S. V.; Soloviev, I. I.; Kupriyanov, M. Y.; Tereshonok, M. V.; Sidorenko, A. S. Beilstein Arch. 2022, 202216. doi:10.3762/bxiv.2022.16.v1

Download Citation
Download RIS (Reference Manager) Download BIB (BIBTEX)

© 2022 Schegolev et al.; licensee Beilstein-Institut.
This is an open access work licensed under the terms of the Beilstein-Institut Open Access License Agreement (https://www.beilstein-archives.org/xiv/terms), which is identical to the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). The reuse of material under this license requires that the author(s), source and license are credited. Third-party material in this work could be subject to other licenses (typically indicated in the credit line), and in this case, users are required to obtain permission from the license holder to reuse the material.

Other Beilstein-Institut Open Science Activities