neuropixels-analysis

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()))