{"ID":2891194,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.17224","arxiv_id":"2507.17224","title":"HuiduRep: A Robust Self-Supervised Framework for Learning Neural Representations from Extracellular Recordings","abstract":"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","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2507.17224","url_pdf":"https://arxiv.org/pdf/2507.17224v3","authors":"[\"Feng Cao\",\"Zishuo Feng\",\"Jicong Zhang\",\"Wei Shi\"]","published":"2025-07-23T05:45:38Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"cs.AI\",\"q-bio.NC\"]","methods":"[]","has_code":false,"code_links":[{"ID":611858,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2891194,"paper_url":"https://arxiv.org/abs/2507.17224","paper_title":"HuiduRep: A Robust Self-Supervised Framework for Learning Neural Representations from Extracellular Recordings","repo_url":"https://github.com/IgarashiAkatuki/HuiduRep","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}