Algorithms; Animals; Models, Neurological; Nonlinear Dynamics; Dimensionality Reduction; Action Potentials/physiology; Neurons/physiology; Signal Processing, Computer-Assisted; Action Potentials; Neurons; Neuroscience (all); Immunology and Microbiology (all); Biochemistry, Genetics and Molecular Biology (all); Agricultural and Biological Sciences (all)
Abstract :
[en] 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 Manifold Approximation and Projection (UMAP) can drastically improve the performance, efficiency, robustness, and scalability of spike sorting pipelines without increasing their computational cost. We show how replacing the linear or ad hoc, expert-defined, supervised nonlinear dimensionality reduction methods commonly used in spike sorting pipelines by the unsupervised, mathematically grounded, nonlinear dimensionality reduction method provided by UMAP drastically increases the number of correctly sorted neurons, makes the identification of quieter, seldom spiking neurons more reliable, enables deeper and more precise explorations and analysis of the neural code, and paves new ways toward more efficient and end-to-end automatable spike sorting pipelines of large-scale extracellular neural recording as those produced by high-density multielectrode arrays.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Suárez-Barrera, Daniel; Instituto de Fisiología Celular-Neurociencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
Bayones, Lucas; Instituto de Fisiología Celular-Neurociencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
Encinas-Rodríguez, Norberto; Instituto de Fisiología Celular-Neurociencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
Parra Sánchez, Sergio ; Université de Liège - ULiège > Département d'électricité, électronique et informatique (Institut Montefiore) > Brain-Inspired Computing ; Université de Liège - ULiège > Montefiore Institute of Electrical Engineering and Computer Science
Monroy, Viktor; Instituto de Fisiología Celular-Neurociencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
Pujalte, Sebastián; Instituto de Fisiología Celular-Neurociencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
Andrade-Ortega, Bernardo; Instituto de Fisiología Celular-Neurociencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
Díaz, Héctor; Instituto de Fisiología Celular-Neurociencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
Alvarez, Manuel; Instituto de Fisiología Celular-Neurociencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
Zainos, Antonio; Instituto de Fisiología Celular-Neurociencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
Franci, Alessio ; Université de Liège - ULiège > Département d'électricité, électronique et informatique (Institut Montefiore) > Brain-Inspired Computing ; WEL Research Institute, Wavre, Belgique
Rossi-Pool, Román ; Instituto de Fisiología Celular-Neurociencias, Universidad Nacional Autónoma de México, Mexico City, Mexico ; Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico City, Mexico
Language :
English
Title :
Efficient and reliable spike sorting from neural recordings with UMAP-based unsupervised nonlinear dimensionality reduction.
UNAM - Universidad Nacional Autónoma de México CONACYT - Consejo Nacional de Ciencia y Tecnología IBRO - International Brain Research Organization
Funding text :
This work was supported by grants PAPIIT-IN210819, PAPIIT-IN205022 and IN203825 (to R. R. -P.) from the Direcci\u00F3n de Asuntos del Personal Acad\u00E9mico de la Universidad Nacional Aut\u00F3noma de M\u00E9xico and CONAHCYT-319347 (to R. R. -P.) from Consejo Nacional de Ciencia y Tecnolog\u00EDa; SECIHTI-CBF-2025-I-2054 (to R. R. -P.) from Secretaria de Ciencia, Humanidades, Tecnolog\u00EDa e Innovaci\u00F3n; IBRO Early Career Award 2022 (to R. R. -P.) from International Brain Research Association. L.B. is a postdoctoral researcher (Postdoctoral fellowship CONACYT-838783). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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