Transfer function of an asymmetric superconducting Gauss neuron

The Gauss neuron is a nonlinear signal converter, whose transfer function (TF) is described by the derivative of some sigmoidal dependence. A superconducting Gauss neuron can be implemented as a two-junction interferometer shunted symmetrically by an additional inductance. This work analyzes three cases of asymmetry that can occur in the experimental samples of Gauss neurons, that is, unequal critical currents of the interferometer's Josephson junctions, asymmetric inductive shunting, and asymmetry of the input signal supply. We illustrate the modifications in equations and the shape of the TF compared to the symmetric case. The analysis performed provides an explanation for the key features observed in a previously conducted experiment.

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Publication Details

Journal
Beilstein Journal of Nanotechnology
Published
2025-07-21
DOI
https://doi.org/10.3762/bjnano.16.85
Citations
1
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
2.48

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article

Transfer function of an asymmetric superconducting Gauss neuron

N. S. Shuravin, С. В. Егоров, M. S. Sidelnikov, L. N. Karelina et al.
1 citations
Beilstein Journal of Nanotechnology
Neural Networks and Applications
2.48
article

Transfer function of an asymmetric superconducting Gauss neuron

N. S. Shuravin, С. В. Егоров, M. S. Sidelnikov, L. N. Karelina, A. S. Ionin, F. A. Razorenov, V. V. Bol’ginov
article en
1 citations

Abstract

The Gauss neuron is a nonlinear signal converter, whose transfer function (TF) is described by the derivative of some sigmoidal dependence. A superconducting Gauss neuron can be implemented as a two-junction interferometer shunted symmetrically by an additional inductance. This work analyzes three cases of asymmetry that can occur in the experimental samples of Gauss neurons, that is, unequal critical currents of the interferometer's Josephson junctions, asymmetric inductive shunting, and asymmetry of the input signal supply. We illustrate the modifications in equations and the shape of the TF compared to the symmetric case. The analysis performed provides an explanation for the key features observed in a previously conducted experiment.

Beilstein Journal of NanotechnologyVol. 16
Moscow Institute of Physics and Technology (RU), Osipyan Institute of Solid State Physics RAS (RU), Quantum Technologies (Sweden) (SE)
Russian Science Foundation, Moscow Institute of Physics and Technology, Russian Academy of Sciences
Openalex Percentile: Top 9%
Neural Networks and Applications
2.48
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Transfer function of an asymmetric superconducting Gauss neuron — N. S. Shuravin, С. В. Егоров, et al. · Beilstein Journal of Nanotechnology (2025) | TGRS Research Map | TGRS