Alterlab neuropixels

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
• 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
• Exporting results to Phy or NWB

Supported Hardware & Formats

Probe	Electrodes	Channels	Notes
Neuropixels 1.0	960	384	Requires phase_shift correction
Neuropixels 2.0 (single)	1280	384	Denser geometry
Neuropixels 2.0 (4-shank)	5120	384	Multi-region recording

Format	Extension	Reader
SpikeGLX	.ap.bin, .lf.bin, .meta	si.read_spikeglx()
Open Ephys	.continuous, .oebin	si.read_openephys()
NWB	.nwb	si.read_nwb()

Quick Start

Basic Import and Setup

import spikeinterface.full as si

# Bundled helper functions live in scripts/neuropixels_pipeline.py
from scripts.neuropixels_pipeline import (
    load_recording, preprocess, check_drift, correct_motion,
    run_spike_sorting, postprocess, curate_units, export_results, run_pipeline,
)

# Configure parallel processing
job_kwargs = dict(n_jobs=-1, chunk_duration='1s', progress_bar=True)

Loading Data

# SpikeGLX (most common)
recording = si.read_spikeglx('/path/to/data', stream_id='imec0.ap')

# Open Ephys (common for many labs)
recording = si.read_openephys('/path/to/Record_Node_101/')

# Check available streams
streams, ids = si.get_neo_streams('spikeglx', '/path/to/data')
print(streams)  # ['imec0.ap', 'imec0.lf', 'nidq']

# For testing with subset of data
recording = recording.frame_slice(0, int(60 * recording.get_sampling_frequency()))

Complete Pipeline (One Command)

# Run full analysis pipeline (writes all outputs under output_path/)
from scripts.neuropixels_pipeline import run_pipeline

run_pipeline(
    data_path='/path/to/data',
    output_path='output/',
    sorter='kilosort4',
    stream_name='imec0.ap',
    apply_motion_correction=True,
    curation_method='allen',
)

# Results are written to disk:
#   output/sorting_output/    spike sorter output
#   output/analyzer/          SortingAnalyzer (waveforms, metrics)
#   output/quality_metrics.csv
#   output/curation_labels.json

Or run it from the command line:

python scripts/neuropixels_pipeline.py /path/to/data output/ --sorter kilosort4 --curation allen

Standard Analysis Workflow

1. Preprocessing

# Recommended preprocessing chain
rec = si.highpass_filter(recording, freq_min=400)
rec = si.phase_shift(rec)  # Required for Neuropixels 1.0
bad_ids, _ = si.detect_bad_channels(rec)
rec = rec.remove_channels(bad_ids)
rec = si.common_reference(rec, operator='median')

# Or use the bundled wrapper (returns the preprocessed recording + bad channel ids)
from scripts.neuropixels_pipeline import preprocess
rec, bad_channels = preprocess(recording)

2. Check and Correct Drift

from scripts.neuropixels_pipeline import check_drift, correct_motion

# Check for drift (always do this!) — detects/localizes peaks and saves
# a drift plot to <output_folder>/drift_check.png, returns a dict with
# 'drift_estimate' (μm range).
drift_info = check_drift(rec, output_folder='output/')

# Apply correction if needed
if drift_info['drift_estimate'] > 20:  # microns
    rec = correct_motion(rec, output_folder='output/', preset='nonrigid_fast_and_accurate')

3. Spike Sorting

# Kilosort4 (recommended, requires GPU)
sorting = si.run_sorter('kilosort4', rec, folder='ks4_output')

# CPU alternatives
sorting = si.run_sorter('tridesclous2', rec, folder='tdc2_output')
sorting = si.run_sorter('spykingcircus2', rec, folder='sc2_output')
sorting = si.run_sorter('mountainsort5', rec, folder='ms5_output')

# Check available sorters
print(si.installed_sorters())

4. Postprocessing

# Create analyzer and compute all extensions
analyzer = si.create_sorting_analyzer(sorting, rec, sparse=True)

analyzer.compute('random_spikes', max_spikes_per_unit=500)
analyzer.compute('waveforms', ms_before=1.0, ms_after=2.0)
analyzer.compute('templates', operators=['average', 'std'])
analyzer.compute('spike_amplitudes')
analyzer.compute('correlograms', window_ms=50.0, bin_ms=1.0)
analyzer.compute('unit_locations', method='monopolar_triangulation')
analyzer.compute('quality_metrics')

metrics = analyzer.get_extension('quality_metrics').get_data()

5. Curation

# 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 (returns {unit_id: 'good'|'mua'|'noise'})
from scripts.neuropixels_pipeline import curate_units
labels = curate_units(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. The recommended workflow is to render per-unit summary plots with SpikeInterface and let Claude inspect them:

import spikeinterface.widgets as sw
import matplotlib.pyplot as plt

# Find borderline units worth a visual look
uncertain = metrics.query('snr > 3 and snr < 8').index.tolist()

# Render a summary figure per uncertain unit (waveform + correlogram + amplitudes)
for unit_id in uncertain:
    sw.plot_unit_summary(analyzer, unit_id=unit_id)
    plt.savefig(f'ai_curation/unit_{unit_id}_summary.png', dpi=150, bbox_inches='tight')
    plt.close()

Claude Code Integration: When running within Claude Code, ask Claude to examine the saved waveform/correlogram plots directly - no API setup required.

7. Generate Analysis Report

# The bundled run_pipeline writes a machine-readable summary.json
# (sampling rate, duration, channel count, unit counts) into output_path/.
import json
with open('output/summary.json') as f:
    summary = json.load(f)
print(summary)

# For a browsable HTML report of waveforms/metrics, use SpikeInterface's exporter:
si.export_report(analyzer, output_folder='output/report/')
# Open output/report/index.html for figures and the per-unit table

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 NWB (via NeuroConv — SpikeInterface has no native NWB exporter)
# pip install neuroconv
from neuroconv.tools.spikeinterface import write_sorting, write_recording
write_recording(recording=rec, nwbfile_path='output.nwb', overwrite=True)
write_sorting(sorting=sorting, nwbfile_path='output.nwb')

# Save quality metrics
metrics.to_csv('quality_metrics.csv')

Common Pitfalls and Best Practices

1. Always check drift before spike sorting - drift > 10μm significantly impacts quality
2. Use phase_shift for Neuropixels 1.0 probes (not needed for 2.0)
3. Save preprocessed data to avoid recomputing - use rec.save(folder='preprocessed/')
4. Use GPU for Kilosort4 - it's 10-50x faster than CPU alternatives
5. Review uncertain units manually - automated curation is a starting point
6. Combine metrics with AI - use metrics for clear cases, AI for borderline units
7. Document your thresholds - different analyses may need different criteria
8. Export to Phy for critical experiments - human oversight is valuable

Key Parameters to Adjust

Preprocessing

• freq_min: Highpass cutoff (300-400 Hz typical)
• detect_threshold: Bad channel detection sensitivity

Motion Correction

• preset: 'kilosort_like' (fast) or 'nonrigid_accurate' (better for severe drift)

Spike Sorting (Kilosort4)

• batch_size: Samples per batch (30000 default)
• nblocks: Number of drift blocks (increase for long recordings)
• Th_learned: Detection threshold (lower = more spikes)

Quality Metrics

• snr_threshold: Signal-to-noise cutoff (3-5 typical)
• isi_violations_ratio: Refractory violations (0.01-0.5)
• presence_ratio: Recording coverage (0.5-0.95)

Bundled Resources

scripts/preprocess_recording.py

Automated preprocessing script:

python scripts/preprocess_recording.py /path/to/data --output preprocessed/

scripts/run_sorting.py

Run spike sorting:

python scripts/run_sorting.py preprocessed/ --sorter kilosort4 --output sorting/

scripts/compute_metrics.py

Compute quality metrics and apply curation:

python scripts/compute_metrics.py sorting/ preprocessed/ --output metrics/ --curation allen

scripts/export_to_phy.py

Export to Phy for manual curation:

python scripts/export_to_phy.py metrics/analyzer --output phy_export/

assets/analysis_template.py

Complete analysis template. Copy and customize:

cp assets/analysis_template.py my_analysis.py
# Edit parameters and run
python my_analysis.py

references/standard_workflow.md

Detailed step-by-step workflow with explanations for each stage.

references/api_reference.md

Quick function reference organized by module.

references/plotting_guide.md

Comprehensive visualization guide for publication-quality figures.

Detailed Reference Guides

Topic	Reference
Full workflow	references/standard_workflow.md
API reference	references/api_reference.md
Plotting guide	references/plotting_guide.md
Preprocessing	references/PREPROCESSING.md
Spike sorting	references/SPIKE_SORTING.md
Motion correction	references/MOTION_CORRECTION.md
Quality metrics	references/QUALITY_METRICS.md
Automated curation	references/AUTOMATED_CURATION.md
AI-assisted curation	references/AI_CURATION.md
Waveform analysis	references/ANALYSIS.md

Installation

# Core packages
pip install spikeinterface[full] probeinterface neo

# Spike sorters
pip install kilosort          # Kilosort4 (GPU required)
pip install spykingcircus     # SpykingCircus2 (CPU)
pip install mountainsort5     # Mountainsort5 (CPU)

# Our toolkit ships as local scripts (scripts/) — no pip install needed;
# run them directly or import from scripts.neuropixels_pipeline

# Optional: AI curation
pip install anthropic

# Optional: IBL tools
pip install ibl-neuropixel ibllib

Project Structure

project/
├── raw_data/
│   └── recording_g0/
│       └── recording_g0_imec0/
│           ├── recording_g0_t0.imec0.ap.bin
│           └── recording_g0_t0.imec0.ap.meta
├── preprocessed/           # Saved preprocessed recording
├── motion/                 # Motion estimation results
├── sorting_output/         # Spike sorter output
├── analyzer/               # SortingAnalyzer (waveforms, metrics)
├── phy_export/             # For manual curation
├── ai_curation/            # AI analysis reports
└── results/
    ├── quality_metrics.csv
    ├── curation_labels.json
    └── output.nwb

Additional Resources

• SpikeInterface Docs: https://spikeinterface.readthedocs.io/
• Neuropixels Tutorial: https://spikeinterface.readthedocs.io/en/stable/how_to/analyze_neuropixels.html
• Kilosort4 GitHub: https://github.com/MouseLand/Kilosort
• IBL Neuropixel Tools: https://github.com/int-brain-lab/ibl-neuropixel
• Allen Institute ecephys: https://github.com/AllenInstitute/ecephys_spike_sorting
• Bombcell (Automated QC): https://github.com/Julie-Fabre/bombcell
• SpikeAgent (AI Curation): https://github.com/SpikeAgent/SpikeAgent