Transfer function of an asymmetric superconducting Gauss neuron

• Fedor A. Razorenov,
• Aleksander S. Ionin,
• Nikita S. Shuravin,
• Liubov N. Karelina,
• Mikhail S. Sidel’nikov,
• Sergey V. Egorov and
• Vitaly V. Bol’ginov

Beilstein J. Nanotechnol. 2025, 16, 1160–1170, doi:10.3762/bjnano.16.85

• inductance of the input circuit, while l− represents the imbalance in the normalized inductances of the Josephson circuits. Note the complete coincidence of Equation 13 and Equation 32, which define the linear relationship between the output signal and the sum phase. The influence of Josephson asymmetry

Tunable superconducting neurons for networks based on radial basis functions

• Andrey E. Schegolev,
• Nikolay V. Klenov,
• Sergey V. Bakurskiy,
• Igor I. Soloviev,
• Mikhail Yu. Kupriyanov,
• Maxim V. Tereshonok and
• Anatoli S. Sidorenko

Beilstein J. Nanotechnol. 2022, 13, 444–454, doi:10.3762/bjnano.13.37

• , ferromagnetic, and normal layers. Keywords: networks on radial basis functions; Josephson circuits; radial basis functions (RBFs); spintronics; superconducting electronics; superconducting neural network; Introduction For modern telecommunications, probabilistic identification of various sources in a

• experimental studies of the features of macroscopic quantum interference in the complex Josephson circuits. The direct use of the previously proposed superconducting adiabatic neural network (ANN) based on the perceptron [44][45][46][47][48] for probabilistic identification is not possible. In particular