Virtual Brain Models imply latent nonlinear state space models
driven by noise and network input, necessitating advanced probabilistic
machinelearning 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.

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