Neuropixels Analysis

Neuropixels Data Analysis

Overview

Toolkit for analyzing Neuropixels high-density neural recordings using current best practices from SpikeInterface, the Allen Institute, and the International Brain Laboratory (IBL). It covers the full workflow from raw data to publication-ready curated units.

All examples use the real SpikeInterface API (spikeinterface.full as si) plus the companion curation module (spikeinterface.curation as sc). The skill ships runnable scripts in scripts/ and a copy-and-edit template in assets/ that implement this workflow directly on top of SpikeInterface — there is no separate package to install beyond the dependencies listed under Installation.

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, common reference, bad-channel detection)
• Detecting and correcting motion/drift
• Running spike sorting (Kilosort4, SpykingCircus2, Mountainsort5, Tridesclous2)
• Computing quality metrics (SNR, ISI violations, presence ratio, amplitude cutoff)
• Curating units (threshold-based, model-based, or AI-assisted)
• Creating visualizations and exporting to Phy or NWB

Supported Hardware & Formats

Quick Start

Import and configure parallel processing

import spikeinterface.full as si

# Global job kwargs are reused by all parallelizable steps
si.set_global_job_kwargs(n_jobs=-1, chunk_duration="1s", progress_bar=True)

Loading data

# Inspect available streams first
stream_names, stream_ids = si.get_neo_streams("spikeglx", "/path/to/run_g0/")
print(stream_names)  # e.g. ['imec0.ap', 'imec0.lf', 'nidq']

# SpikeGLX (most common) — select the AP stream by name
recording = si.read_spikeglx("/path/to/run_g0/", stream_name="imec0.ap", load_sync_channel=False)

# Open Ephys
recording = si.read_openephys("/path/to/Record_Node_101/")

# For quick iteration, slice the first 60 s
fs = recording.get_sampling_frequency()
recording_sub = recording.frame_slice(0, int(60 * fs))

Full pipeline (bundled script)

The repository ships an end-to-end pipeline built on SpikeInterface:

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

It performs load → preprocess → drift check → optional motion correction → sorting → postprocessing → quality metrics → curation → export. Read the steps below to run them interactively or customize the pipeline.

Standard Analysis Workflow

1. Preprocessing

Recommended chain, following the SpikeInterface Neuropixels how-to (IBL-style destriping with channel removal + common reference):

rec = si.highpass_filter(recording, freq_min=400.0)
bad_channel_ids, channel_labels = si.detect_bad_channels(rec)
rec = rec.remove_channels(bad_channel_ids)
rec = si.phase_shift(rec)  # ADC phase correction (Neuropixels 1.0)
rec = si.common_reference(rec, operator="median", reference="global")

Save the preprocessed recording (Kilosort needs a binary file, and it speeds up reuse):

rec = rec.save(folder="preprocessed/", format="binary")

2. Check and correct drift

Always inspect drift before sorting:

from spikeinterface.sortingcomponents.peak_detection import detect_peaks
from spikeinterface.sortingcomponents.peak_localization import localize_peaks

noise_levels = si.get_noise_levels(rec, return_in_uV=False)
peaks = detect_peaks(rec, method="locally_exclusive", noise_levels=noise_levels,
                     detect_threshold=5, radius_um=50.0)
peak_locations = localize_peaks(rec, peaks, method="center_of_mass")

# Visualize the drift raster
si.plot_drift_raster_map(peaks=peaks, peak_locations=peak_locations,
                         recording=rec, clim=(-50, 50))

Apply correction if needed (presets: rigid_fast, kilosort_like, nonrigid_accurate, nonrigid_fast_and_accurate, dredge, dredge_fast):

rec_corrected = si.correct_motion(rec, preset="nonrigid_fast_and_accurate", folder="motion/")

3. Spike sorting

# Kilosort4 (recommended, requires a CUDA GPU)
sorting = si.run_sorter("kilosort4", rec_corrected, folder="ks4_output")

# CPU alternatives (internally developed, no external install)
sorting = si.run_sorter("spykingcircus2", rec_corrected, folder="sc2_output")
sorting = si.run_sorter("tridesclous2", rec_corrected, folder="tdc2_output")
sorting = si.run_sorter("mountainsort5", rec_corrected, folder="ms5_output")

# External sorters can run in containers without local install
sorting = si.run_sorter("kilosort2_5", rec_corrected, folder="ks25_output", docker_image=True)

print(si.installed_sorters())

Note: run_sorter uses the folder= argument. The older output_folder= is deprecated.

4. Postprocessing

analyzer = si.create_sorting_analyzer(sorting, rec_corrected, sparse=True,
                                      format="binary_folder", folder="analyzer/")

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

metric_names = ["firing_rate", "presence_ratio", "snr", "isi_violation", "amplitude_cutoff"]
analyzer.compute("quality_metrics", metric_names=metric_names)
metrics = analyzer.get_extension("quality_metrics").get_data()

5. Curation by metric thresholds

# Allen-style query (note: column is isi_violations_ratio)
query = "(amplitude_cutoff < 0.1) & (isi_violations_ratio < 0.5) & (presence_ratio > 0.9)"
good_unit_ids = metrics.query(query).index.values

For reusable, multi-threshold logic with allen / ibl / strict presets, use the bundled scripts/compute_metrics.py. See references/AUTOMATED_CURATION.md for details and the Bombcell / UnitMatch tools.

6. Model-based curation (UnitRefine)

SpikeInterface can apply pretrained machine-learning classifiers from Hugging Face via the spikeinterface.curation module. The UnitRefine models were trained on real Neuropixels data (V1, SC, ALM):

import spikeinterface.curation as sc

# 1) noise vs neural
noise_labels = sc.model_based_label_units(
    sorting_analyzer=analyzer,
    repo_id="SpikeInterface/UnitRefine_noise_neural_classifier",
    trust_model=True,
)
neural = analyzer.remove_units(noise_labels[noise_labels["prediction"] == "noise"].index)

# 2) single-unit (sua) vs multi-unit (mua) on the surviving units
sua_mua_labels = sc.model_based_label_units(
    sorting_analyzer=neural,
    repo_id="SpikeInterface/UnitRefine_sua_mua_classifier",
    trust_model=True,
)

Each call returns a DataFrame with prediction and probability (confidence) per unit. trust_model=True (or an explicit trusted=[...] list) is required to load the .skops model — only load models from sources you trust. Models trained on other brain areas/datasets may not transfer; validate against a manually labelled subset.