This tool was developed at INS in Marseille.
Authors: M Hashemi, A Ziaeemehr, MM Woodman, J Fousek, S Petkoski, VK Jirsa

Virtual Brain Models imply latent nonlinear state space models
driven by noise and network input, necessitating advanced probabilistic
machine learning techniques for widely applicable Bayesian estimation.
Here we present Simulation-based Inference on Virtual Brain Models (SBI-VBMs),
and demonstrate that training deep neural networks on both spatio-temporal and
functional features allows for accurate estimation of generative parameters in brain disorders.

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

Ref: https://iopscience.iop.org/article/10.1088/2632-2153/ad6230

@article{SBI-VBM,
  title={Simulation-based inference on virtual brain models of disorders},
  author={Hashemi, Meysam and Ziaeemehr, Abolfazl and Woodman, Marmaduke M and Fousek, Jan and Petkoski, Spase and Jirsa, Viktor K},
  journal={Machine Learning: Science and Technology},
  volume={5},
  number={3},
  pages={035019},
  year={2024},
  publisher={IOP Publishing}
}