neuro-py¶

Analysis of neuroelectrophysiology data in Python. Built on top of nelpy for core data objects, neuro_py provides functions for freely moving electrophysiology analysis — including behavior tracking, neural ensemble detection, peri-event analyses, spectral methods, and robust batch analysis tools.

Getting Started¶

Installation

git clone
cd neuro_py
pip install -e .

To sync the nelpy dependency to latest version, use following instead,

pip install -e . --force-reinstall --no-cache-dir

Quick Start
```
import neuro_py as npy
```

Core Modules¶

Behavior

Track and linearize animal positions, classify well traversals, and compute kinematics from freely moving recordings.

View Module
Ensemble

Detect neural assemblies, measure reactivation, compute explained variance, decode position, and analyze replay events.

View Module
LFP

Spectral analysis, current source density (CSD), theta cycle detection, and LFP preprocessing pipelines.

View Module
Process

Peri-event time histograms, cross-correlations, interval manipulation, batch analysis, and signal utilities.

View Module
Spikes

Spike train tools for computing firing rates, burst detection, and other single-unit analysis utilities.

View Module
Tuning

Spatial tuning maps, place field detection, and receptive field analysis for spatial coding studies.

View Module

More Modules¶

Detectors

Automated detection of dentate spikes, UP/DOWN states, and other electrophysiological events.

View Module
Stats

Circular statistics, regression models, system identification, and statistical testing utilities.

View Module
Plotting

Publication-ready figure helpers, event raster plots, and plotting decorators for consistent styling.

View Module
I/O

Load and save electrophysiology data — spikes, LFP, epochs, ripples, sleep states, and CellExplorer formats.

View Module
Raw

Raw data preprocessing, spike sorting progress tracking, and Phy log parsing utilities.

View Module
Session

Locate behavioral epochs, identify pre/task/post structure, and manage recording session metadata.

View Module

Tutorials¶

Learn by example with our hands-on notebooks:

Attractor Landscape

Visualize neural attractor dynamics and energy landscapes.

View Tutorial
Batch Analysis

Run analyses across multiple sessions with parallel batch tools.

View Tutorial
Bias Correlation

Compute pairwise bias correlations across behavioral epochs.

View Tutorial
Decoding

Decode position and variables from neural population activity.

View Tutorial
Explained Variance

Measure how much sleep activity is explained by prior experience.

View Tutorial
Lfp Loader Tutorial

Explore the Lfp Loader Tutorial tutorial notebook.

View Tutorial
Neural Geodynamics

Explore geometric dynamics of neural population activity.

View Tutorial
Peri-Event Analysis

Align neural activity to behavioral events with PETHs.

View Tutorial
Reactivation

Detect and quantify neural assembly reactivation during sleep.

View Tutorial
Spatial Maps

Build place fields and spatial tuning curves from freely moving data.

View Tutorial

Built With¶

Python 3.10+

numpy

scipy

matplotlib

scikit-learn

pandas

numba

tqdm

seaborn

nelpy

PyWavelets

lightning

h5py

Community¶

GitHub

Star or watch the repository to stay updated with the latest changes.

View Repository
Contributing

Pull requests welcome. For major changes, please open an issue first to discuss what you would like to change.

Open an Issue
Testing

Run the test suite to verify everything works:
```
pytest
```