ArticleeNeuro2024
A Modular Implementation to Handle and Benchmark Drift Correction for High-Density Extracellular Recordings.
Article in eNeuro, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Structured experience shapes strategy learning and neural dynamics in the medial entorhinal cortex.Nature neuroscience · 2026Article
- Article
- Efficient and reproducible pipelines for spike sorting large-scale electrophysiology data.eLife · 2026Article
- KIASORT: Knowledge-Integrated Automated Spike Sorting for Geometry-Free Neuron Tracking.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Benchmarking spike source localization algorithms in high density probes.PLoS computational biology · 2026Article
- Tau drives cell specific functional isolation of the hippocampal formation.bioRxiv : the preprint server for biology · 2025Article
- DREDge: robust motion correction for high-density extracellular recordings across species.Nature methods · 2025Article
- Article
- Is the impact of spontaneous movements on early visual cortex species specific?Trends in neurosciences · 2025Review
- DREDge: robust motion correction for high-density extracellular recordings across species.bioRxiv : the preprint server for biology · 2023Article
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Authors and funding
6 authors.
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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 a benchmark study to precisely and quantitatively evaluate the performance of several state-of-the-art motion correction algorithms introduced in the literature. Using simulated recordings with induced drifts, we dissect the origins of the errors performed while applying a 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.
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