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

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 (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

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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

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© 2022 Schegolev et al.; licensee Beilstein-Institut.
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