Which spike sorters are compatible?

It integrates with Kilosort4 (recommended for GPU users), SpykingCircus2, Mountainsort5, and Tridesclous2 via the SpikeInterface framework.

What data formats can I load?

It natively supports SpikeGLX (.bin/.meta), Open Ephys (.oebin/.continuous), and Neurodata Without Borders (NWB) formats.

Can it handle probe drift correction?

Yes, the skill includes tools for estimating and correcting non-rigid probe drift using both 'kilosort_like' and 'nonrigid_accurate' motion correction presets.

Does this skill support Neuropixels 2.0?

Yes, it supports Neuropixels 1.0, 2.0 single-shank, and 2.0 multi-shank (4-shank) probe configurations.

Neuropixels Data Analysis

Name: Neuropixels Data Analysis
Author: pur3v4d3r

bypur3v4d3r

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데이터 과학 및 ML

Streamlines high-density extracellular electrophysiology workflows from raw recording ingestion to publication-ready unit curation.

소개

This skill provides a comprehensive toolkit for analyzing Neuropixels 1.0 and 2.0 neural recordings, implementing industry-standard best practices from SpikeInterface and the International Brain Laboratory (IBL). It automates complex electrophysiology workflows including phase-shift correction, non-rigid motion estimation, and high-performance spike sorting using Kilosort4. Beyond automated processing, the skill facilitates rigorous quality control through SNR and ISI violation metrics, integrated AI-assisted visual analysis for unit classification, and seamless data export to Phy or NWB formats for downstream research.

주요 기능

AI-assisted unit curation and automated report generation for neural data
Seamless integration with Kilosort4, SpykingCircus2, and Mountainsort5 sorters
Automated preprocessing with phase-shift correction and common average referencing
Comprehensive quality metric calculation including SNR, ISI violations, and presence ratio
1 GitHub stars
Advanced non-rigid motion and drift correction for high-density recordings

사용 사례

Detecting and correcting probe drift in long-duration neural recordings to recover lost units
Standardizing quality control metrics across multi-lab electrophysiology datasets
Processing raw SpikeGLX or Open Ephys data into curated, publication-ready spike-sorted units

소개

주요 기능

AI-assisted unit curation and automated report generation for neural data
Seamless integration with Kilosort4, SpykingCircus2, and Mountainsort5 sorters
Automated preprocessing with phase-shift correction and common average referencing
Comprehensive quality metric calculation including SNR, ISI violations, and presence ratio
1 GitHub stars
Advanced non-rigid motion and drift correction for high-density recordings

사용 사례

Detecting and correcting probe drift in long-duration neural recordings to recover lost units
Standardizing quality control metrics across multi-lab electrophysiology datasets
Processing raw SpikeGLX or Open Ephys data into curated, publication-ready spike-sorted units