pyDANT 1.0.2

A Python package for tracking neurons across days

pyDANT: A Python toolbox for Density-based Across-day Neuron Tracking

pyDANT is a Python toolbox designed for the robust, longitudinal tracking of neurons across multiple recording sessions using high-density probes.

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📄 Preprint

Density-based longitudinal neuron tracking in high-density electrophysiological recordings

📚 Read the Documentation

🐍 Check out the MATLAB version (DANT)

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Installation

This section describes installation of the pyDANT.

Install with Anaconda

Anaconda is recommended for managing the pyDANT environment.

conda create -n pyDANT python=3.11
conda activate pyDANT
pip install pyDANT

Install from Python Package Index (PyPI)

You can also install pyDANT directly from PyPI:

pip install pyDANT

Install from Source

If you prefer to install from source, clone the repository and install it manually:

git clone https://github.com/jiumao2/pyDANT.git
cd pyDANT
pip install -e .

🚀 Getting Started

To help you get familiar with the pipeline, we have provided an example dataset and a step-by-step walkthrough.

1. Download the Data: Example Dataset for pyDANT (Figshare)
2. Run the Pipeline: Follow our comprehensive Tutorial to run the example data or process your own recordings.

If you encounter any bugs, have questions, or want to suggest a feature, please open an issue. We look forward to your feedback!

📝 Citation

If you use pyDANT in your research, please cite our preprint:

@article {Huang2025DANT,
    author = {Huang, Yue and Wang, Hanbo and Cao, Jiaming and Chen, Yu and Wang, Xuanning and Zhao, Yujie and Ren, Hengkun and Zheng, Qiang and Yu, Jianing},
    title = {Density-based longitudinal neuron tracking in high-density electrophysiological recordings},
    year = {2025},
    doi = {10.64898/2025.12.19.695632},
    publisher = {Cold Spring Harbor Laboratory},
    URL = {https://www.biorxiv.org/content/early/2025/12/23/2025.12.19.695632},
    journal = {bioRxiv}
}

📚 References & Acknowledgements

pyDANT builds upon and integrates several excellent open-source tools. We extend our gratitude to the authors of the following packages:

• HDBSCAN: Hierarchical Density-Based Spatial Clustering of Applications with Noise. (Campello et al., 2013; McInnes & Healy, 2017).
• Kilosort: Fast spike sorting with drift correction. (Pachitariu et al., 2024).
• DREDge: Robust online multiband drift estimation in electrophysiology data. (Windolf et al., 2025).

📄 License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.