corresponding to Schäfer et al., 2025, bioRxiv: Data-driven burst shape analysis for functional phenotyping of neuronal cultures
@article{schaefer2025data-driven,
author = {Sch{\"a}fer, Tim J. and Giannakakis, Emmanouil and Schmidt-Barbo, Paul and Levina, Anna and Vinogradov, Oleg},
title = {Data-driven burst shape analysis for functional phenotyping of neuronal cultures},
year = {2025},
doi = {10.1101/2025.09.29.679256},
journal = {bioRxiv},
}
notebooks/tutorial.ipynb walks you through the basic pipeline step-by-step.
You can also try out the analysis pipeline without installing anything using the following online tools.
Try burst visualization (10s loading time)! This is used to visualize all recordings and for adjusting burst detection hyperparameters.
Try embedding visualization (10s loading time)! This is used for visualizing the spectral embedding (of individual burst shapes) and exploring this burst shape space.
- Blocked inhibition --- Bicuculline (data: Vinogradov et al., 2024)
- Kleefstra syndrom (hPSC) (data: Mossink et al., 2021)
- CACNA1A mutation (data: Hommersom et al., 2024)
- Burst visualization (data not public yet)
- Embedding visualization (data not public yet)
- Developing cultures (data: Wagenaar et al., 2006)
- Burst visualization (dataset too large)
- Embedding visualization (dataset too large)
[Optional] Create a conda environment named burst-shape and python=3.11 with
conda create -n burst-shape python=3.11Activate the environment with
conda activate burst-shapeInstall src module with
pip install -e .which will run setup.py, making the src module available.
[Optional] If this fails to install the dependencies, you can install them manually with
pip install -r requirements.txt
