pyKilomatch

A Python toolbox for tracking the neurons across days.

This project is a Python implementation of Kilomatch, converted from the original MATLAB code. Read the documentation for more details.

Installation

• It is recommended to install the pyKilomatch package using Anaconda:

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

How to use it

Please follow the tutorial to run your dataset.

Please raise an issue if you meet any bugs or have any questions. We are looking forward to your feedback!

References

HDBSCAN
HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over varying epsilon values and integrates the result to find a clustering that gives the best stability over epsilon. This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN), and be more robust to parameter selection.

Campello, R.J.G.B., Moulavi, D., Sander, J. (2013). Density-Based Clustering Based on Hierarchical Density Estimates. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7819. Springer, Berlin, Heidelberg. Density-Based Clustering Based on Hierarchical Density Estimates

L. McInnes and J. Healy, (2017). Accelerated Hierarchical Density Based Clustering. In: IEEE International Conference on Data Mining Workshops (ICDMW), 2017, pp. 33-42. Accelerated Hierarchical Density Based Clustering

Kilosort
Fast spike sorting with drift correction

Pachitariu, Marius, Shashwat Sridhar, Jacob Pennington, and Carsen Stringer. “Spike Sorting with Kilosort4.” Nature Methods 21, no. 5 (May 2024): 914–21. https://doi.org/10.1038/s41592-024-02232-7.

DREDge
Robust online multiband drift estimation in electrophysiology data

Windolf, Charlie, Han Yu, Angelique C. Paulk, Domokos Meszéna, William Muñoz, Julien Boussard, Richard Hardstone, et al. “DREDge: Robust Motion Correction for High-Density Extracellular Recordings across Species.” Nature Methods, March 6, 2025. https://doi.org/10.1038/s41592-025-02614-5.

License

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