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- W4382515512 abstract "Abstract High-density neural devices are now offering the possibility to record from neuronal populations in-vivo at unprecedented scale. However, the mechanical drifts often observed in these recordings are currently a major issue for “spike sorting”, an essential analysis step to identify the activity of single neurons from extracellular signals. Although several strategies have been proposed to compensate for such drifts, the lack of proper benchmarks makes it hard to assess the quality and effectiveness of motion correction. In this paper, we present an exhaustive benchmark study to precisely and quantitatively evaluate the performance of several state-of-the-art motion correction algorithms introduced in literature. Using simulated recordings with induced drifts, we dissect the origins of the errors performed while applying motion-correction algorithm as a preprocessing step in the spike sorting pipeline. We show how important it is to properly estimate the positions of the neurons from extracellular traces in order to correctly estimate the probe motion, compare several interpolation procedures, and highlight what are the current limits for motion correction approaches. Significance statement" @default.
- W4382515512 created "2023-06-30" @default.
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- W4382515512 date "2023-06-29" @default.
- W4382515512 modified "2023-10-18" @default.
- W4382515512 title "A modular approach to handle<i>in-vivo</i>drift correction for high-density extracellular recordings" @default.
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- W4382515512 doi "https://doi.org/10.1101/2023.06.29.546882" @default.
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