Advancing Spike Sorting Through Gradient-Based Preprocessing and Nonlinear Reduction With Agglomerative Clustering

M. Lotfi

doi:10.1002/brb3.70650

Advancing Spike Sorting Through Gradient-Based Preprocessing and Nonlinear Reduction With Agglomerative Clustering

2025-07-03

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M. Lotfi

F

Fatemeh Zareayan Jahromy

M

Mohammad Reza Daliri *

DOI：10.1002/brb3.70650

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Spike sorting is the process of separating electrical events produced by individual neurons in the nervous system, known as “spikes.” Accurate spike sorting is vital because it significantly impacts the reliability of all future analyses. Although several semi-automated and fully automated spike-sorting algorithms have been developed, their classification accuracy often proves insufficient. This has led researchers to resort to manual sorting, despite its time-consuming and labor-intensive nature. In certain conditions and for specific neuron populations, manual sorting can also be inefficient due to the presence of visually indistinguishable similarities between spikes. This underscores the necessity for the development of fully automated spike-sorting methods capable of achieving high accuracy.

Keywords:

optimal features

spectral embedding

spike sorting

uniform manifold approximation and projection (UMAP)

Advancing Spike Sorting Through Gradient-Based Preprocessing and Nonlinear Reduction With Agglomerative Clustering

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