Tunable superconducting neurons for networks based on radial basis functions

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.