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.
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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.
Neuropixels Data Analysis
Overview
Comprehensive toolkit for analyzing Neuropixels high-density neural recordings using current best practices from SpikeInterface, Allen Institute, and International Brain Laboratory (IBL). Supports the full workflow from raw data to publication-ready curated units.
When to Use This Skill
This skill should be used when:
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
# Allen Institute criteria (conservative)
good_units = metrics.query("""
presence_ratio > 0.9 and
isi_violations_ratio < 0.5 and
amplitude_cutoff < 0.1
""").index.tolist()
# Or use automated curation
labels = npa.curate(metrics, method='allen') # 'allen', 'ibl', 'strict'
6. AI-Assisted Curation (For Uncertain Units)
When using this skill with Claude Code, Claude can directly analyze waveform plots and provide expert curation decisions. For programmatic API access:
from anthropic import Anthropic
# Setup API client
client = Anthropic()
# Analyze uncertain units visually
uncertain = metrics.query('snr > 3 and snr < 8').index.tolist()
for unit_id in uncertain:
result = npa.analyze_unit_visually(analyzer, unit_id, api_client=client)
print(f"Unit {unit_id}: {result['classification']}")
print(f" Reasoning: {result['reasoning'][:100]}...")
Claude Code Integration: When running within Claude Code, ask Claude to examine waveform/correlogram plots directly - no API setup required.
7. Generate Analysis Report
# Generate comprehensive HTML report with visualizations
report_dir = npa.generate_analysis_report(results, 'output/')
# Opens report.html with summary stats, figures, and unit table# Print formatted summary to console
npa.print_analysis_summary(results)
8. Export Results
# Export to Phy for manual review
si.export_to_phy(analyzer, output_folder='phy_export/',
compute_pc_features=True, compute_amplitudes=True)
# Export to NWBfrom spikeinterface.exporters import export_to_nwb
export_to_nwb(rec, sorting, 'output.nwb')
# Save quality metrics
metrics.to_csv('quality_metrics.csv')
