@article {70256,
	title = {Model-agnostic linear-memory online learning in spiking neural networks.},
	journal = {Nat Commun},
	year = {2026},
	month = {2026 Jan 19},
	abstract = {<p>Spiking neural networks (SNNs) offer a promising paradigm for modeling brain dynamics and developing neuromorphic intelligence, yet an online learning system capable of training rich spiking dynamics over long horizons with low memory footprints has been missing. Existing online approaches either incur quadratic memory growth, sacrifice biological fidelity through oversimplified models, or lack end-to-end automated tooling. Here, we introduce BrainTrace, a model-agnostic, linear-memory, and automated online learning system for spiking neural networks. BrainTrace standardizes model specification to encompass diverse neuronal and synaptic dynamics; implements a linear-memory online learning rule by exploiting intrinsic properties of spiking dynamics; and provides a compiler that automatically generates optimized online-learning code for arbitrary user-defined models. Across diverse dynamics and tasks, BrainTrace achieves strong learning performance with a low memory footprint and high computational throughput. Critically, these properties enable online fitting of a whole-brain-scale Drosophila SNN that recapitulates region-level functional activity. By reconciling generality, efficiency, and usability, BrainTrace establishes a foundation for spiking network modeling at scale.</p>
},
	issn = {2041-1723},
	doi = {10.1038/s41467-026-68453-w},
	url = { https://doi.org/10.1038/s41467-026-68453-w},
	author = {Wang, Chaoming and Dong, Xingsi and Ji, Zilong and Xiao, Mingqing and Jiang, Jiedong and Liu, Xiao and Huan, Yuxiang and Wu, Si}
}
