Sparse Coding-based Multichannel Spike Sorting with the Locally Competitive Algorithm

Melot, Alexis; Alibart, Fabien; Yger, Pierre; Wood, Sean

Type de document :

Communication dans un congrès avec actes

DOI :

10.1109/BioCAS58349.2023.10388594

Titre :

Sparse Coding-based Multichannel Spike Sorting with the Locally Competitive Algorithm

Auteur(s) :

Melot, Alexis [Auteur]
Laboratoire Nanotechnologies et Nanosystèmes [Sherbrooke] [LN2]
Alibart, Fabien [Auteur]

Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Nanostructures, nanoComponents & Molecules - IEMN [NCM - IEMN]
Laboratoire Nanotechnologies et Nanosystèmes [Sherbrooke] [LN2]
Yger, Pierre [Auteur]

Institut de la Vision
Lille Neurosciences & Cognition - U 1172 [LilNCog]
Unité de neurosciences intégratives et computationnelles [UNIC]
Institut de Neurobiologie Alfred Fessard [INAF]
Wood, Sean [Auteur]
Université de Sherbrooke [UdeS]

Titre de la manifestation scientifique :

2023 IEEE Biomedical Circuits and Systems Conference (BioCAS)

Ville :

Toronto

Pays :

Canada

Date de début de la manifestation scientifique :

2023-10-19

Éditeur :

IEEE

Date de publication :

2024-01-18

Mot(s)-clé(s) en anglais :

locally competitive algorithm
sparse coding
spike sorting
dictionary learning

Discipline(s) HAL :

Physique [physics]
Sciences de l'ingénieur [physics]

Résumé en anglais : [en]

Spike sorting is a crucial step in the analysis of multichannel neural signals that enables the identification of individual neurons’ activity. However, the limited availability of low-power neuromorphic spike sorting ...
Lire la suite >Spike sorting is a crucial step in the analysis of multichannel neural signals that enables the identification of individual neurons’ activity. However, the limited availability of low-power neuromorphic spike sorting methods is due to the difficulty of processing high-density multichannel extracellular neural signals. In this study, we propose to use the locally competitive algorithm (LCA) that has been previously implemented on neuromorphic hardware as a novel feature extraction method for spike sorting. Based on a bio-inspired neural network, LCA can learn a signal-dependent dictionary of spatiotemporal features and give highly sparse representations. The proposed approach results in better sorting accuracy at low signal-to-noise ratios compared to k-SVD, a well-known sparse coding model, and the principal component analysis (PCA), a classical approach in spike sorting. This network-based solution paves the way for neuromorphic processing of multichannel neural signals in future brain implants.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Commentaire :

https://github.com/NECOTIS/LCA-Spike-Sorting

Collections :

Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL

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