AI-Assisted Curation Reference

AI-Assisted Curation Reference

Guide to using AI visual analysis for unit curation, inspired by SpikeAgent's approach.

Overview

AI-assisted curation uses vision-language models to analyze spike sorting visualizations, providing expert-level quality assessments similar to human curators.

Workflow

Traditional:  Metrics → Threshold → Labels
AI-Enhanced:  Metrics → AI Visual Analysis → Confidence Score → Labels

Claude Code Integration

When using this skill within Claude Code, Claude can directly analyze waveform plots without requiring API setup. Simply:

1. Generate a unit report or plot
2. Ask Claude to analyze the visualization
3. Claude will provide expert-level curation decisions

Example workflow in Claude Code:

# Generate plots for a unit
npa.plot_unit_summary(analyzer, unit_id=0, output='unit_0_summary.png')

# Then ask Claude: "Please analyze this unit's waveforms and autocorrelogram
# to determine if it's a well-isolated single unit, multi-unit activity, or noise"

Claude can assess:

• Waveform consistency and shape
• Refractory period violations from autocorrelograms
• Amplitude stability over time
• Overall unit isolation quality

Quick Start

Generate Unit Report

import neuropixels_analysis as npa

# Create visual report for a unit
report = npa.generate_unit_report(analyzer, unit_id=0, output_dir='reports/')

# Report includes:
# - Waveforms, templates, autocorrelogram
# - Amplitudes over time, ISI histogram
# - Quality metrics summary
# - Base64 encoded image for API

AI Visual Analysis

from anthropic import Anthropic

# Setup API client
client = Anthropic()

# Analyze single unit
result = npa.analyze_unit_visually(
    analyzer,
    unit_id=0,
    api_client=client,
    model='claude-opus-4.5',
    task='quality_assessment'
)

print(f"Classification: {result['classification']}")
print(f"Reasoning: {result['reasoning']}")

Batch Analysis

# Analyze all units
results = npa.batch_visual_curation(
    analyzer,
    api_client=client,
    output_dir='ai_curation/',
    progress_callback=lambda i, n: print(f"Progress: {i}/{n}")
)

# Get labels
ai_labels = {uid: r['classification'] for uid, r in results.items()}

Interactive Curation Session

For human-in-the-loop curation with AI assistance:

# Create session
session = npa.CurationSession.create(
    analyzer,
    output_dir='curation_session/',
    sort_by_confidence=True  # Show uncertain units first
)

# Process units
while True:
    unit = session.current_unit()
    if unit is None:
        break

    print(f"Unit {unit.unit_id}:")
    print(f"  Auto: {unit.auto_classification} (conf: {unit.confidence:.2f})")

    # Generate report
    report = npa.generate_unit_report(analyzer, unit.unit_id)

    # Get AI opinion
    ai_result = npa.analyze_unit_visually(analyzer, unit.unit_id, api_client=client)
    session.set_ai_classification(unit.unit_id, ai_result['classification'])

    # Human decision
    decision = input("Decision (good/mua/noise/skip): ")
    if decision != 'skip':
        session.set_decision(unit.unit_id, decision)

    session.next_unit()

# Export results
labels = session.get_final_labels()
session.export_decisions('final_curation.csv')

Analysis Tasks

Quality Assessment (Default)

Analyzes waveform shape, refractory period, amplitude stability.

result = npa.analyze_unit_visually(analyzer, uid, task='quality_assessment')
# Returns: 'good', 'mua', or 'noise'

Merge Candidate Detection

Determines if two units should be merged.

result = npa.analyze_unit_visually(analyzer, uid, task='merge_candidate')
# Returns: 'merge' or 'keep_separate'

Drift Assessment

Evaluates motion/drift in the recording.

result = npa.analyze_unit_visually(analyzer, uid, task='drift_assessment')
# Returns drift magnitude and correction recommendation

Custom Prompts

Create custom analysis prompts:

from neuropixels_analysis.ai_curation import create_curation_prompt

# Get base prompt
prompt = create_curation_prompt(
    task='quality_assessment',
    additional_context='Focus on waveform amplitude consistency'
)

# Or fully custom
custom_prompt = """
Analyze this unit and determine if it represents a fast-spiking interneuron.

Look for:
1. Narrow waveform (peak-to-trough < 0.5ms)
2. High firing rate
3. Regular ISI distribution

Classify as: FSI (fast-spiking interneuron) or OTHER
"""

result = npa.analyze_unit_visually(
    analyzer, uid,
    api_client=client,
    custom_prompt=custom_prompt
)

Combining AI with Metrics

Best practice: use both AI and quantitative metrics:

def hybrid_curation(analyzer, metrics, api_client):
    """Combine metrics and AI for robust curation."""
    labels = {}

    for unit_id in metrics.index:
        row = metrics.loc[unit_id]

        # High confidence from metrics alone
        if row['snr'] > 10 and row['isi_violations_ratio'] < 0.001:
            labels[unit_id] = 'good'
            continue

        if row['snr'] < 1.5:
            labels[unit_id] = 'noise'
            continue

        # Uncertain cases: use AI
        result = npa.analyze_unit_visually(
            analyzer, unit_id, api_client=api_client
        )
        labels[unit_id] = result['classification']

    return labels

Session Management

Resume Session

# Resume interrupted session
session = npa.CurationSession.load('curation_session/20250101_120000/')

# Check progress
summary = session.get_summary()
print(f"Progress: {summary['progress_pct']:.1f}%")
print(f"Remaining: {summary['remaining']} units")

# Continue from where we left off
unit = session.current_unit()

Navigate Session

# Go to specific unit
session.go_to_unit(42)

# Previous/next
session.prev_unit()
session.next_unit()

# Update decision
session.set_decision(42, 'good', notes='Clear refractory period')

Export Results

# Get final labels (priority: human > AI > auto)
labels = session.get_final_labels()

# Export detailed results
df = session.export_decisions('curation_results.csv')

# Summary
summary = session.get_summary()
print(f"Good: {summary['decisions'].get('good', 0)}")
print(f"MUA: {summary['decisions'].get('mua', 0)}")
print(f"Noise: {summary['decisions'].get('noise', 0)}")

Visual Report Components

The generated report includes 6 panels:

API Support

Currently supported APIs:

Anthropic Example

from anthropic import Anthropic

client = Anthropic(api_key="your-api-key")
result = npa.analyze_unit_visually(analyzer, uid, api_client=client)

OpenAI Example

from openai import OpenAI

client = OpenAI(api_key="your-api-key")
result = npa.analyze_unit_visually(
    analyzer, uid,
    api_client=client,
    model='gpt-4-vision-preview'
)

Best Practices

1. Use AI for uncertain cases - Don't waste API calls on obvious good/noise units
2. Combine with metrics - AI should supplement, not replace, quantitative measures
3. Human oversight - Review AI decisions, especially for important analyses
4. Save sessions - Always use CurationSession to track decisions
5. Document reasoning - Use notes field to record decision rationale

Cost Optimization

# Only use AI for uncertain units
uncertain_units = metrics.query("""
    snr > 2 and snr < 8 and
    isi_violations_ratio > 0.001 and isi_violations_ratio < 0.1
""").index.tolist()

# Batch process only these
results = npa.batch_visual_curation(
    analyzer,
    unit_ids=uncertain_units,
    api_client=client
)

References

• SpikeAgent - AI-powered spike sorting assistant
• Anthropic Vision API
• GPT-4 Vision