Spike sorting is a crucial step to extract information from extracellular recordings and even a prerequisite for studying many types of brain function. This project aims at investigating several existing spike sorting methods, which include both offline and real-time processing and use either supervised or unsupervised learning algorithms, and at comparing their performances with different types and scales of neural data.
University of Rochester
Spring 2019
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