Pattern recognition with superconducting wirelet neurons

Abstract

Neuromorphic computing aims to reproduce the energy efficiency and adaptability of biological intelligence in hardware. Superconducting devices are an attractive platform due to their ultra-low dissipation and fast switching dynamics. Here we employ a resistively shunted superconducting wirelet as a minimal artificial neuron for temporal neuromorphic computation. This simple architecture enables straightforward fabrication, electronic control, and high scalability. Through experiments and advanced simulations, we show that it exhibits spiking voltage dynamics driven by the interplay of resistive switching and relaxation, with threshold, firing frequency, and refractory time tunable through applied current, temperature, and shunt resistance. We demonstrate neural network operation by synaptically training temporal voltage signals generated by individual neurons. In this approach, trainable temporal weights act directly on the time-dependent wirelet-neuron responses rather than on static neuron outputs alone, allowing the computation to exploit the full temporal structure of the superconducting spikes. As an illustrative example, we apply this framework to handwritten digit recognition and show accurate classification using only three superconducting wirelet neurons. We further discuss on-chip training based on related gated wirelets as tunable synaptic elements, establishing shunted superconducting wirelets as scalable, energy-efficient building blocks for cryogenic artificial intelligence hardware that can be integrated with other emerging superconducting technologies.