Efficient and reliable spike sorting from neural recordings with UMAP-based unsupervised nonlinear dimensionality reduction.

ABSTRACT: Spike sorting is one of the cornerstones of extracellular electrophysiology. By leveraging advanced signal processing and data analysis techniques, spike sorting makes it possible to detect, isolate, and map single neuron spiking activity from both in vivo and in vitro extracellular electrophysiological recordings. A crucial step of any spike sorting pipeline is to reduce the dimensionality of the recorded spike waveform data. Reducing the dimensionality of the processed data is a near-universal practice, fundamentally motivated by the use of clustering algorithms responsible to detect, isolate, and sort the recorded putative neurons. In this paper, we propose and illustrate on both synthetic and experimental data that employing the nonlinear dimensionality reduction technique Uniform Mani

SUBMITTER: Suarez-Barrera D 

PROVIDER: S-EPMC12671831 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature