/neuropixels-analysis

name: neuropixels-analysis
description: Analyze Neuropixels extracellular recordings end-to-end with SpikeInterface. Covers loading SpikeGLX/Open Ephys/NWB data, preprocessing, drift/motion correction, Kilosort4 (and CPU) spike sorting, quality metrics, and unit curation (threshold-based, model-based UnitRefine, and AI-assisted visual review). Use when working with Neuropixels 1.0/2.0 recordings, spike sorting, or extracellular electrophysiology analysis.
license: MIT license
metadata:
  version: "2.4"
  skill-author: K-Dense Inc.
  openclaw:
    primaryEnv: ANTHROPIC_API_KEY
    envVars:
    - name: ANTHROPIC_API_KEY
      required: false
      description: For optional Claude API calls.

Neuropixels Data Analysis

Overview

Toolkit for analyzing Neuropixels high-density neural recordings using current best practices from [SpikeInterface](https://spikeinterface.readthedocs.io/), 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](#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

| Probe | Electrodes | Channels | Notes | |-------|-----------|----------|-------| | Neuropixels 1.0 | 960 | 384 | Use `phase_shift` for ADC 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

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_corre