Programmable superconducting neuron with intrinsic in-memory computation and dual-timescale plasticity for ultra-efficient neuromorphic computing
Abstract
The escalating energy demands of artificial intelligence pose a critical challenge to conventional computing. Leveraging the efficiency of event-driven, in-memory neuromorphic architectures into the superconducting circuits with ultra-high speed and low power dissipation advantages offers a promising solution to energy-efficient computing. However, the potential of such a solution has yet to be realized, owning to the absence of a fundamental superconducting unit that unifies programmability, local memory, and multi-timescale plasticity. Here, we introduce a programmable Josephson-junction-based leaky integrate-and-fire (LIF) neuron that features intrinsic static memory and precise programmability by encoding somatic and synaptic parameters directly in the bias current. This neuron is also capable of dual-timescale plasticity: picosecond-scale short-term modulation of spike transmission and long-term weight retention exceeding 10,000 seconds, facilitating both rapid temporal adaptation and robust weight storage. It can operate up to 45 GHz with femtojoule-level energy dissipation per spike, and supports 10 somatic threshold levels and 20 synaptic states. Furthermore, we demonstrate a crossbar-based spiking neural network (SNN) utilizing this neuron, which achieves outstanding performance across multiple tasks. By fusing computation, memory and plasticity into a single superconducting unit, our work paves the way for the next generation of ultrafast, energy-efficient neuromorphic computing.
Cite
@article{arxiv.2603.04966,
title = {Programmable superconducting neuron with intrinsic in-memory computation and dual-timescale plasticity for ultra-efficient neuromorphic computing},
author = {Muen Wang and Shucheng Yang and Yuxiang Lin and Yuntian Gao and Xue Zhang and Xiaoping Gao and Minghui Niu and Huanli Liu and Yikang Wan and Wei Peng and Jie Ren},
journal= {arXiv preprint arXiv:2603.04966},
year = {2026}
}