domains / families / science / neuropixels-analysis

neuropixels-analysis

combined

Neuropixels neural recording analysis. Load SpikeGLX/OpenEphys data, preprocess, motion correction, Kilosort4 spike sorting, quality metrics, Allen/IBL curation, AI-assisted visual analysis, for Neuropixels 1.0/2.0 extracellular electrophysiology. Use when working with neural recordings, spike sorting, extracellular electrophysiology, or when the user mentions Neuropixels, SpikeGLX, Open Ephys, Kilosort, quality metrics, or unit curation. Analyzes Neuropixels extracellular recordings end-to-end with SpikeInterface. Covers loading SpikeGLX/Open Ephys/NWB data, preprocessing, drift/motion correction, Kilosort4 (and CPU) spike sorting, quality metrics, and unit curation (threshold-based, model-based UnitRefine, and AI-assisted visual review). Use when working with Neuropixels 1.0/2.0 recordings, spike sorting, or extracellular electrophysiology analysis.

When to use

• Working with Neuropixels recordings (.ap.bin, .lf.bin, .meta files)
• Loading data from SpikeGLX, Open Ephys, or NWB formats
• Preprocessing neural recordings (filtering, CAR, bad channel detection)
• Detecting and correcting motion/drift in recordings
• Running spike sorting (Kilosort4, SpykingCircus2, Mountainsort5)
• Computing quality metrics (SNR, ISI violations, presence ratio)
• Curating units using Allen/IBL criteria
• Creating visualizations of neural data

Skill page

Conflict

The copies describe the same high-level workflow (load, preprocess, drift correction, sort, metrics, curation) but diverge significantly on implementation details. The K-Dense-AI copy is the most rigorous, using the native SpikeInterface API (`si`) and specific version constraints (0.105.0), while the other three copies (Vibe-Skills, medical-research-skills, OpenClaw-Medical-Skills) rely on a hypothetical `neuropixels_analysis` (npa) wrapper package that is not part of the standard SpikeInterface distribution. The K-Dense copy also includes specific hardware-aware constraints (e.g., shank-specific referencing) that the others omit or simplify. Because the 'wrapper' approach in the other copies is a distinct (and potentially non-functional or different) implementation path compared to the 'native API' path in K-Dense, crowning one would hide the alternative implementation strategy.