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Neuropixels数据分析工具包：SpikeInterface全流程处理，支持Kilosort4尖峰排序与Allen/IBL标准筛选

neuropixels-analysis by k-dense-ai/claude-scientific-skills

114 周安装量

17,800 GitHub Stars

GitHub

安装命令

npx skills add https://github.com/k-dense-ai/claude-scientific-skills --skill neuropixels-analysis

数据分析科研工具生物信息学

🇨🇳中文介绍

Neuropixels 数据分析

概述

使用 SpikeInterface、Allen Institute 和国际脑实验室 (IBL) 当前最佳实践，全面分析 Neuropixels 高密度神经记录的工具包。支持从原始数据到可发表、经过筛选的单元的全流程工作流。

何时使用此技能

此技能应在以下情况使用：

处理 Neuropixels 记录数据（.ap.bin、.lf.bin、.meta 文件）
从 SpikeGLX、Open Ephys 或 NWB 格式加载数据
预处理神经记录（滤波、公共平均参考、坏通道检测）
检测和校正记录中的运动/漂移
运行尖峰排序（Kilosort4、SpykingCircus2、Mountainsort5）
计算质量指标（信噪比、ISI 违规、存在率）
使用 Allen/IBL 标准筛选单元
创建神经数据的可视化
将结果导出到 Phy 或 NWB

支持的硬件与格式

探针	电极数	通道数	备注
Neuropixels 1.0	960	384	需要 phase_shift 校正
Neuropixels 2.0（单探针）	1280	384	更密集的几何结构
Neuropixels 2.0（4 探针）	5120	384	多区域记录

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基本导入与设置

import spikeinterface.full as si
import neuropixels_analysis as npa

# 配置并行处理
job_kwargs = dict(n_jobs=-1, chunk_duration='1s', progress_bar=True)

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

# Open Ephys（许多实验室常用）
recording = si.read_openephys('/path/to/Record_Node_101/')

# 检查可用数据流
streams, ids = si.get_neo_streams('spikeglx', '/path/to/data')
print(streams)  # ['imec0.ap', 'imec0.lf', 'nidq']

# 用于测试数据子集
recording = recording.frame_slice(0, int(60 * recording.get_sampling_frequency()))

完整流程（单条命令）

# 运行完整分析流程
results = npa.run_pipeline(
    recording,
    output_dir='output/',
    sorter='kilosort4',
    curation_method='allen',
)

# 访问结果
sorting = results['sorting']
metrics = results['metrics']
labels = results['labels']

标准分析工作流

# 推荐的预处理链
rec = si.highpass_filter(recording, freq_min=400)
rec = si.phase_shift(rec)  # Neuropixels 1.0 必需
bad_ids, _ = si.detect_bad_channels(rec)
rec = rec.remove_channels(bad_ids)
rec = si.common_reference(rec, operator='median')

# 或使用我们的包装函数
rec = npa.preprocess(recording)

2. 检查并校正漂移

# 检查漂移（务必执行此步骤！）
motion_info = npa.estimate_motion(rec, preset='kilosort_like')
npa.plot_drift(rec, motion_info, output='drift_map.png')

# 如果需要则应用校正
if motion_info['motion'].max() > 10:  # 微米
    rec = npa.correct_motion(rec, preset='nonrigid_accurate')

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

# CPU 替代方案
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')

# 检查已安装的排序器
print(si.installed_sorters())

# 创建分析器并计算所有扩展
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()

# Allen Institute 标准（保守）
good_units = metrics.query("""
    presence_ratio > 0.9 and
    isi_violations_ratio < 0.5 and
    amplitude_cutoff < 0.1
""").index.tolist()

# 或使用自动筛选
labels = npa.curate(metrics, method='allen')  # 'allen', 'ibl', 'strict'

6. AI 辅助筛选（针对不确定单元）

在 Claude Code 中使用此技能时，Claude 可以直接分析波形图并提供专家筛选决策。对于编程 API 访问：

from anthropic import Anthropic

# 设置 API 客户端
client = Anthropic()

# 可视化分析不确定单元
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 集成：在 Claude Code 中运行时，可直接要求 Claude 检查波形/相关图——无需设置 API。

7. 生成分析报告

# 生成包含可视化的综合性 HTML 报告
report_dir = npa.generate_analysis_report(results, 'output/')
# 打开 report.html，其中包含摘要统计、图表和单元表格

# 将格式化摘要打印到控制台
npa.print_analysis_summary(results)

# 导出到 Phy 进行人工审查
si.export_to_phy(analyzer, output_folder='phy_export/',
                 compute_pc_features=True, compute_amplitudes=True)

# 导出到 NWB
from spikeinterface.exporters import export_to_nwb
export_to_nwb(rec, sorting, 'output.nwb')

# 保存质量指标
metrics.to_csv('quality_metrics.csv')

常见陷阱与最佳实践

尖峰排序前务必检查漂移——漂移 > 10μm 会显著影响质量
对 Neuropixels 1.0 探针使用 phase_shift（2.0 不需要）
保存预处理数据以避免重复计算——使用 rec.save(folder='preprocessed/')
对 Kilosort4 使用 GPU——比 CPU 替代方案快 10-50 倍
人工审查不确定单元——自动筛选只是一个起点
将指标与 AI 结合使用——对明确情况使用指标，对边界单元使用 AI
记录你的阈值——不同的分析可能需要不同的标准
对关键实验导出到 Phy——人工监督很有价值

需调整的关键参数

freq_min：高通截止频率（通常为 300-400 Hz）
detect_threshold：坏通道检测灵敏度

preset：'kilosort_like'（快速）或 'nonrigid_accurate'（对严重漂移效果更好）

尖峰排序（Kilosort4）

batch_size：每批样本数（默认 30000）
nblocks：漂移块数（对长记录增加此值）
Th_learned：检测阈值（越低 = 尖峰越多）

snr_threshold：信噪比截止值（通常为 3-5）
isi_violations_ratio：不应期违规（0.01-0.5）
presence_ratio：记录覆盖率（0.5-0.95）

scripts/preprocess_recording.py

自动化预处理脚本：

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

scripts/run_sorting.py

运行尖峰排序：

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

scripts/compute_metrics.py

计算质量指标并应用筛选：

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

scripts/export_to_phy.py

导出到 Phy 进行人工筛选：

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

assets/analysis_template.py

完整的分析模板。复制并自定义：

cp assets/analysis_template.py my_analysis.py
# 编辑参数并运行
python my_analysis.py

reference/standard_workflow.md

详细的逐步工作流，包含每个阶段的解释。

reference/api_reference.md

按模块组织的快速函数参考。

reference/plotting_guide.md

用于发表质量图表的全面可视化指南。

主题	参考
完整工作流	references/standard_workflow.md
API 参考	references/api_reference.md
绘图指南	references/plotting_guide.md
预处理	references/PREPROCESSING.md
尖峰排序	references/SPIKE_SORTING.md
运动校正	references/MOTION_CORRECTION.md
质量指标	references/QUALITY_METRICS.md
自动筛选	references/AUTOMATED_CURATION.md
AI 辅助筛选	references/AI_CURATION.md
波形分析	references/ANALYSIS.md

# 核心包
pip install spikeinterface[full] probeinterface neo

# 尖峰排序器
pip install kilosort          # Kilosort4（需要 GPU）
pip install spykingcircus     # SpykingCircus2（CPU）
pip install mountainsort5     # Mountainsort5（CPU）

# 我们的工具包
pip install neuropixels-analysis

# 可选：AI 筛选
pip install anthropic

# 可选：IBL 工具
pip install ibl-neuropixel ibllib

project/
├── raw_data/
│   └── recording_g0/
│       └── recording_g0_imec0/
│           ├── recording_g0_t0.imec0.ap.bin
│           └── recording_g0_t0.imec0.ap.meta
├── preprocessed/           # 保存的预处理记录
├── motion/                 # 运动估计结果
├── sorting_output/         # 尖峰排序器输出
├── analyzer/               # SortingAnalyzer（波形、指标）
├── phy_export/             # 用于人工筛选
├── ai_curation/            # AI 分析报告
└── results/
    ├── quality_metrics.csv
    ├── curation_labels.json
    └── output.nwb

2026 年 1 月 20 日

🇺🇸English

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	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

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

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Repository

k-dense-ai/clau…c-skills

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First Seen

Jan 20, 2026

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DOCX文件创建、编辑与分析完整指南 - 使用docx-js、Pandoc和Python脚本

55,800 周安装

Neuropixels数据分析工具包：SpikeInterface全流程处理，支持Kilosort4尖峰排序与Allen/IBL标准筛选

🇨🇳中文介绍

Neuropixels 数据分析

概述

何时使用此技能

支持的硬件与格式

相关 Skills

快速开始

基本导入与设置

加载数据

完整流程（单条命令）

标准分析工作流

1. 预处理

2. 检查并校正漂移

3. 尖峰排序

4. 后处理

5. 筛选

6. AI 辅助筛选（针对不确定单元）

7. 生成分析报告

8. 导出结果

常见陷阱与最佳实践

需调整的关键参数

预处理

运动校正

尖峰排序（Kilosort4）

质量指标

捆绑资源

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

详细参考指南

安装

项目结构

附加资源

🇺🇸English

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

最新 Skills