Towards a Pseudo-Labeling Workflow for Celltype-Classification from Explanted Brain Slice Recordings

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.