HuiduRep: A Robust Self-Supervised Framework for Learning Neural Representations from Extracellular Recordings

TL;DR

HuiduRep introduces a self-supervised framework combining contrastive learning and denoising autoencoders to improve spike sorting robustness from extracellular recordings, outperforming existing methods.

Contribution

The paper presents a novel self-supervised learning approach for extracting noise-robust neural representations, enhancing spike sorting without requiring ground truth labels.

Findings

HuiduRep achieves superior robustness to noise and drift.

The method outperforms KiloSort4 and MountainSort5.

Effective in real-world and hybrid datasets.

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