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
import neuropixels_analysis as npa

# 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
results = npa.run_pipeline(
    recording,
    output_dir='output/',
    sorter='kilosort4',
    curation_method='allen',
)

# Access results
sorting = results['sorting']
metrics = results['metrics']
labels = results['labels']

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 our wrapper
rec = npa.preprocess(recording)

2. Check and Correct Drift

# Check for drift (always do this!)
motion_info = npa.estimate_motion(rec, preset='kilosort_like')
npa.plot_drift(rec, motion_info, output='drift_map.png')

# Apply correction if needed
if motion_info['motion'].max() > 10:  # microns
    rec = npa.correct_motion(rec, preset='nonrigid_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
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 NWB
from spikeinterface.exporters import export_to_nwb
export_to_nwb(rec, sorting, 'output.nwb')

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

Common Pitfalls and Best Practices

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

reference/standard_workflow.md

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

reference/api_reference.md

Quick function reference organized by module.

reference/plotting_guide.md

Comprehensive visualization guide for publication-quality figures.

Detailed Reference Guides

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

# 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

Neuropixels Data Analysis

Overview

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
import neuropixels_analysis as npa

# 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
results = npa.run_pipeline(
    recording,
    output_dir='output/',
    sorter='kilosort4',
    curation_method='allen',
)

# Access results
sorting = results['sorting']
metrics = results['metrics']
labels = results['labels']

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 our wrapper
rec = npa.preprocess(recording)

2. Check and Correct Drift

# Check for drift (always do this!)
motion_info = npa.estimate_motion(rec, preset='kilosort_like')
npa.plot_drift(rec, motion_info, output='drift_map.png')

# Apply correction if needed
if motion_info['motion'].max() > 10:  # microns
    rec = npa.correct_motion(rec, preset='nonrigid_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
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 NWB
from spikeinterface.exporters import export_to_nwb
export_to_nwb(rec, sorting, 'output.nwb')

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

Common Pitfalls and Best Practices

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

reference/standard_workflow.md

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

reference/api_reference.md

Quick function reference organized by module.

reference/plotting_guide.md

Comprehensive visualization guide for publication-quality figures.

Detailed Reference Guides

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

# 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

neuropixels-analysis

让 AI 帮你安装

Neuropixels Data Analysis

Overview

When to Use This Skill

Supported Hardware & Formats

Quick Start

Basic Import and Setup

Loading Data

Complete Pipeline (One Command)

Standard Analysis Workflow

1. Preprocessing

2. Check and Correct Drift

3. Spike Sorting

4. Postprocessing

5. Curation

6. AI-Assisted Curation (For Uncertain Units)

7. Generate Analysis Report

8. Export Results

Common Pitfalls and Best Practices

Key Parameters to Adjust

Preprocessing

Motion Correction

Spike Sorting (Kilosort4)

Quality Metrics

Bundled Resources

scripts/preprocess_recording.py

scripts/run_sorting.py

scripts/compute_metrics.py

scripts/export_to_phy.py

assets/analysis_template.py

reference/standard_workflow.md

reference/api_reference.md

reference/plotting_guide.md

Detailed Reference Guides

Installation

Project Structure

Additional Resources

链接

创作者

Neuropixels Data Analysis

Overview

When to Use This Skill

Supported Hardware & Formats

Quick Start

Basic Import and Setup

Loading Data

Complete Pipeline (One Command)

Standard Analysis Workflow

1. Preprocessing

2. Check and Correct Drift

3. Spike Sorting

4. Postprocessing

5. Curation

6. AI-Assisted Curation (For Uncertain Units)

7. Generate Analysis Report

8. Export Results

Common Pitfalls and Best Practices

Key Parameters to Adjust

Preprocessing

Motion Correction

Spike Sorting (Kilosort4)

Quality Metrics

Bundled Resources

scripts/preprocess_recording.py

scripts/run_sorting.py

scripts/compute_metrics.py

scripts/export_to_phy.py

assets/analysis_template.py

reference/standard_workflow.md

reference/api_reference.md

reference/plotting_guide.md

Detailed Reference Guides

Installation

Project Structure

Additional Resources