Comparison of feature-based and Uniform Manifold Approximation and Projection (UMAP)-based spike sorting for neuron classification.

(A) High-pass filtering (500 Hz–2 kHz) removes low-frequency noise from extracellular signals. (B) Thresholding the pronounced deflections in the filtered data identifies spikes. (C) Windows around each spike, centered on the trough,...

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Bibliografiset tiedot
Päätekijä:	Daniel Suárez-Barrera (22676654) (author)
Muut tekijät:	Lucas Bayones (22676657) (author), Norberto Encinas-Rodríguez (22676660) (author), Sergio Parra (22676663) (author), Viktor Monroy (22676666) (author), Sebastián Pujalte (22676669) (author), Bernardo Andrade-Ortega (22676672) (author), Héctor Díaz (22676675) (author), Manuel Alvarez (3468647) (author), Antonio Zainos (22676678) (author), Alessio Franci (143351) (author), Román Rossi-Pool (22676681) (author)
Julkaistu:	2025
Aiheet:	Cell Biology Molecular Biology Neuroscience Physiology Biotechnology Developmental Biology Science Policy Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified spike sorting pipelines spike sorting pipeline seldom spiking neurons recorded putative neurons correctly sorted neurons clustering algorithms responsible spike sorting makes reliable spike sorting umap drastically increases data analysis techniques drastically improve processed data experimental data universal practice precise explorations neural recordings neural code mathematically grounded fundamentally motivated extracellular electrophysiology enables deeper crucial step computational cost ad hoc
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Comparison of feature-based and Uniform Manifold Approximation and Projection (UMAP)-based spike sorting for neuron classification.

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