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

• URL: http://arxiv.org/abs/2607.06569v1
• Date: Mon, 15 Jun 2026 09:58:57 GMT
• Title: Towards a Pseudo-Labeling Workflow for Celltype-Classification from Explanted Brain Slice Recordings
• Abstract summary: This paper proposes an unsupervised workflow to pseudo-label extracellular spikes from human brain slice MEA recordings into two putative cell types.<n>The raw data from the data acquisition system is used and processed.<n>The pipeline for pre-processing includes bandpass filtering, threshold-based spike detection, frame alignment and normalization.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

Related papers

• KARMA: Knowledge graph-based Automated Reasoning Materialization and Alignment [1.0323063834827415]
  template-based contrastive synthesis is scalable, but its candidates differ only in a few entity-slots.<n>We formalize this as the Resolution Mismatch Problem and propose KARMA.<n>It enumerates schema-constrained paths over domain knowledge graphs and verbalizes them into slot-aligned contrastive candidates.
  arXiv  Detail & Related papers  (2026-07-03T10:06:40Z)
• bioLeak: Leakage-Aware Modeling and Diagnostics for Machine Learning in R [0.0]
  BioLeak is an R package for constructing leakage-aware resampling and auditing fitted models for common leakage mechanisms.<n>The implementation supports binary classification, multiclass classification, regression, and survival analysis, with task-specific metrics and S4 containers for splits, fits, audits, and inflation summaries.
  arXiv  Detail & Related papers  (2026-04-13T04:01:31Z)
• Cluster-First Labelling: An Automated Pipeline for Segmentation and Morphological Clustering in Histology Whole Slide Images [0.0]
  We present a cloud-native, end-to-end pipeline for labelling tissue components in whole slide images.<n>Our system filters out tiles deemed unlikely to contain valuable information.<n>It segments tissue components with Cellpose-SAM, extracts neural embeddings via a pretrained ResNet-50, reduces dimensionality with UMAP, and groups morphologically similar objects using DBSCAN clustering.<n>Our system achieves a weighted cluster-label alignment accuracy of 96.8%, with 7 of 13 tissue types reaching perfect agreement.
  arXiv  Detail & Related papers  (2026-04-10T14:40:36Z)
• Efficient Conformal Volumetry for Template-Based Segmentation [23.593891761167374]
  We introduce ConVOLT, a framework that achieves efficient volumetric uncertainty quantification.<n>ConVOLT calibrates a learned volumetric scaling factor from deformation space features.<n>Our work paves way to exploit the registration process for efficient UQ in medical imaging pipelines.
  arXiv  Detail & Related papers  (2026-02-28T20:16:15Z)
• Unsupervised Conformal Inference: Bootstrapping and Alignment to Control LLM Uncertainty [49.19257648205146]
  We propose an unsupervised conformal inference framework for generation.<n>Our gates achieve close-to-nominal coverage and provide tighter, more stable thresholds than split UCP.<n>The result is a label-free, API-compatible gate for test-time filtering.
  arXiv  Detail & Related papers  (2025-09-26T23:40:47Z)
• GLiClass: Generalist Lightweight Model for Sequence Classification Tasks [49.2639069781367]
  We propose GLiClass, a novel method that adapts the GLiNER architecture for sequence classification tasks.<n>Our approach achieves strong accuracy and efficiency comparable to embedding-based methods, while maintaining the flexibility needed for zero-shot and few-shot learning scenarios.
  arXiv  Detail & Related papers  (2025-08-11T06:22:25Z)
• Self-supervised Latent Space Optimization with Nebula Variational Coding [87.20343320266215]
  This paper proposes a variational inference model which leads to a clustered embedding.<n>We introduce additional variables in the latent space, called textbfnebula anchors, that guide the latent variables to form clusters during training.<n>Since each latent feature can be labeled with the closest anchor, we also propose to apply metric learning in a self-supervised way to make the separation between clusters more explicit.
  arXiv  Detail & Related papers  (2025-06-02T08:13:32Z)
• Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision [75.1860418333995]
  Programmatic Weak Supervision (PWS) has emerged as a widespread paradigm to synthesize training labels efficiently. The core component of PWS is the label model, which infers true labels by aggregating the outputs of multiple noisy supervision sources as labeling functions. Existing statistical label models typically rely only on the outputs of LF, ignoring the instance features when modeling the underlying generative process.
  arXiv  Detail & Related papers  (2022-10-06T07:28:53Z)
• Self-Adaptive Label Augmentation for Semi-supervised Few-shot Classification [121.63992191386502]
  Few-shot classification aims to learn a model that can generalize well to new tasks when only a few labeled samples are available. We propose a semi-supervised few-shot classification method that assigns an appropriate label to each unlabeled sample by a manually defined metric. A major novelty of SALA is the task-adaptive metric, which can learn the metric adaptively for different tasks in an end-to-end fashion.
  arXiv  Detail & Related papers  (2022-06-16T13:14:03Z)
• Missing Data Infill with Automunge [0.0]
  Missing data is a fundamental obstacle in the practice of data science. This paper surveys a few conventions for imputation as available in the Automunge open source python library platform.
  arXiv  Detail & Related papers  (2022-02-19T00:49:30Z)
• Semi-Supervised Learning with Normalizing Flows [54.376602201489995]
  FlowGMM is an end-to-end approach to generative semi supervised learning with normalizing flows. We show promising results on a wide range of applications, including AG-News and Yahoo Answers text data.
  arXiv  Detail & Related papers  (2019-12-30T17:36:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.