ArticleBeilstein journal of nanotechnology2022
Tunable superconducting neurons for networks based on radial basis functions.
Article in Beilstein journal of nanotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Superconducting artificial neural networks and quantum circuits.Beilstein journal of nanotechnology · 2026Article
- Capabilities of the 3D-MLSI software tool in superconducting neuron design.Beilstein journal of nanotechnology · 2026Article
- Programmable soliton dynamics in all-Josephson-junction logic cells and networks.Beilstein journal of nanotechnology · 2025Article
- Transfer function of an asymmetric superconducting Gauss neuron.Beilstein journal of nanotechnology · 2025Article
- Optimisation Challenge for a Superconducting Adiabatic Neural Network That Implements XOR and OR Boolean Functions.Nanomaterials (Basel, Switzerland) · 2024Article
- Spin-Valve-Controlled Triggering of Superconductivity.Nanomaterials (Basel, Switzerland) · 2024Article
- Tunnel Josephson Junction with Spin-Orbit/Ferromagnetic Valve.Nanomaterials (Basel, Switzerland) · 2023Article
- A bifunctional superconducting cell as flux qubit and neuron.Beilstein journal of nanotechnology · 2023Article
- Superconducting Valve Exploiting Interplay between Spin-Orbit and Exchange Interactions.Nanomaterials (Basel, Switzerland) · 2022Article
- Ultrafast signatures of magnetic inhomogeneity in PdBeilstein journal of nanotechnology · 2022Article
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7 authors.
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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. 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.
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