Date of Award
2026-05-01
Degree Name
Master of Science
Department
Computational Science
Advisor(s)
Alexander Friedman
Abstract
Modern extracellular neural probes like Neuropixels, allow for high-density extracellular neural probes to record terabytes of electrophysiological brain data. Spike sorting is the process of isolating individual neurons on this data through various clustering and filtering techniques. Modern spike sorting algorithms are held back by their need for human intervention to separate high-quality from low quality clusters, due to the volume of data that can be collected a completely automated approach is required. To address this requirement, we have developed a framework to preprocess, cluster, grade, and separate high/low quality clustering results using custom models trained on the grades. To test the performance of our framework, we created stationary simulated high-density extracellular recordings (created using the Spike-Interface framework Buccino, et al., 2020). Here we describe the algorithm steps and outline the success of our models in separating high//low quality recordings without the requirement of human intervention.
Language
en
Provenance
Received from ProQuest
Copyright Date
2026-05
File Size
41 p.
File Format
application/pdf
Rights Holder
Luis David Davila
Recommended Citation
Davila, Luis David, "Spike-Sorting Algorithm For Neuropixel Probes" (2026). Open Access Theses & Dissertations. 4658.
https://scholarworks.utep.edu/open_etd/4658