This open-source tool, called DCM_PPLs, was developed at INS in Marseille.

Authors: Nina Baldy, Marmaduke Woodman, Viktor Jirsa, Meysam Hashemi

The aim was to provide inference services for Dynamical Causal Modeling of Event-Related Potentials (ERPs) measured with EEG/MEG, using SATO Probabilistic Programming Languages (PPLs):

Numpyro: https://num.pyro.ai/en/stable/

Blackjax: https://blackjax-devs.github.io/blackjax/

PyMC: https://www.pymc.io/welcome.html

Stan: https://mc-stan.org/

We have provided a taxonomy for model comparison tailored to algorithms: (1) adaptive Hamiltonian Monte Carlo, (2) automatic Laplace and (3) family of variational inference. We have provided solutions to address the deference by: 1) optimizing the hyperparameters, (2) leveraging initialization with prior information, (3) weighted stacking based on predictive accuracy.

Github: https://github.com/ins-amu/DCM_PPLs

Notebooks:

https://wiki.ebrains.eu/bin/view/Collabs/ebrains-task-3-3/Drive#notebooks/DCM_ERP_NumPyro

Tutorial:

https://wiki.ebrains.eu/bin/view/Collabs/ebrains-task-3-3/Drive#notebooks/EITN_tutorial 

@article{DCM_PPLs,
author = {Baldy, Nina  and Woodman, Marmaduke  and Jirsa, Viktor K.  and Hashemi, Meysam},
title = {Dynamic causal modelling in probabilistic programming languages},
journal = {Journal of The Royal Society Interface},
volume = {22},
number = {227},
pages = {20240880},
year = {2025},
doi = {10.1098/rsif.2024.0880},
}