自动为脑片电生理数据打伪标签，区分锥体细胞与中间神经元。
Towards a Pseudo-Labeling Workflow for Celltype-Classification from Explanted Brain Slice Recordings
- 用滤波、阈值检测等预处理提取尖峰信号，再通过聚类分析分类型。
- 严格筛选下，聚类分离度提升，但能识别的细胞数减少。
- 适合神经科学中无标注脑电数据的自动化分类任务。
本文提出一种无监督工作流，将人脑切片多电极阵列（MEA）记录的胞外尖峰信号伪标记为两类潜在细胞类型：锥体细胞和中间神经元。原始数据经带通滤波、基于阈值的尖峰检测、帧对齐与归一化预处理。机器学习流程包括主成分分析（PCA）、t-SNE、UMAP降维，以及高斯混合模型（GMM）、k-means聚类。为实现在线系统，还考察了模板匹配与OSort在不同人工校正严格度下的表现。所有流程通过组内皮尔逊相关性、轮廓系数（Silhouette score）和Calinski-Harabasz指数评估聚类质量。结果显示，更严格的校正可提升聚类分离效果，但牺牲了数据包容性。
原文摘要 · Abstract (English)
This paper proposes an unsupervised workflow to pseudo-label extracellular spikes from human brain slice MEA recordings into two putative cell types: pyramidal cells and interneurons. Here, the raw data from the data acquisition system is used and processed. The pipeline for pre-processing includes bandpass filtering, threshold--based spike detection, frame alignment and normalization. In the ML workflow, dimensionality reduction (PCA, t-SNE, UMAP), clustering (GMM, k-means). To achieve an online system, template matching and OSort under varying curation strictness is also considered. All pipelines are evaluated by different cluster quality with within-cluster Pearson correlation, Silhouette score, and Calinski-Harabasz index. Applying stricter curation improves separation at some cost to inclusivity.
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