自监督学习让神经电记录更抗噪，无需标签也能精准分峰。
HuiduRep: A Robust Self-Supervised Framework for Learning Neural Representations from Extracellular Recordings
- 用对比学习+去噪自编码器，从噪声中提取稳定特征
- 在真实数据上比KiloSort4、MountainSort5更准，跨会话鲁棒性更强
- 适合低信噪比或电极漂移场景，为无监督脑信号分析奠基
细胞外记录是神经元附近短暂的电压波动，是实现单神经元分辨率脑活动解码的基础模态。尖峰排序将检测到的每个尖峰归因于其对应的神经元，是脑感知流程中的关键步骤。然而，在低信噪比、电极漂移和跨会话变异条件下仍具挑战。本文提出HuiduRep，一种鲁棒的自监督表示学习框架，能从细胞外记录中提取判别性强且泛化能力好的特征。通过整合对比学习与去噪自编码器，HuiduRep学习到对噪声和漂移鲁棒的潜在表示。基于HuiduRep，我们构建了一个无需真值标签的尖峰排序流水线，通过聚类尖峰表示完成分类。在混合数据集和真实世界数据集上的实验表明，HuiduRep具有强鲁棒性，且性能超越KiloSort4和MountainSort5等前沿工具。这些结果证明自监督尖峰表示学习在稳健、通用处理细胞外记录方面的潜力。代码已开源：https://github.com/IgarashiAkatuki/HuiduRep
原文摘要 · Abstract (English)
Extracellular recordings are transient voltage fluctuations in the vicinity of neurons, serving as a fundamental modality in neuroscience for decoding brain activity at single-neuron resolution. Spike sorting, the process of attributing each detected spike to its corresponding neuron, is a pivotal step in brain sensing pipelines. However, it remains challenging under low signal-to-noise ratio (SNR), electrode drift and cross-session variability. In this paper, we propose HuiduRep, a robust self-supervised representation learning framework that extracts discriminative and generalizable features from extracellular recordings. By integrating contrastive learning with a denoising autoencoder, HuiduRep learns latent representations that are robust to noise and drift. With HuiduRep, we develop a spike sorting pipeline that clusters spike representations without ground truth labels. Experiments on hybrid and real-world datasets demonstrate that HuiduRep achieves strong robustness. Furthermore, the pipeline outperforms state-of-the-art tools such as KiloSort4 and MountainSort5. These findings demonstrate the potential of self-supervised spike representation learning as a foundational tool for robust and generalizable processing of extracellular recordings. Code is available at: https://github.com/IgarashiAkatuki/HuiduRep
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