Sparse Coding-based Multichannel Spike Sorting with the Locally Competitive Algorithm - Archive ouverte HAL
Communication Dans Un Congrès Année : 2024

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

Résumé

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.
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Dates et versions

hal-04477408 , version 1 (26-02-2024)

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Alexis Melot, Fabien Alibart, Pierre Yger, Sean Wood. Sparse Coding-based Multichannel Spike Sorting with the Locally Competitive Algorithm. 2023 IEEE Biomedical Circuits and Systems Conference (BioCAS), Oct 2023, Toronto, Canada. pp.1-5, ⟨10.1109/BioCAS58349.2023.10388594⟩. ⟨hal-04477408⟩
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