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1 {{{Virtual Brain Models imply latent nonlinear state space models
2 driven by noise and network input, necessitating advanced probabilistic
3 machine learning techniques for widely applicable Bayesian estimation.
4 Here we present Simulation-based Inference on Virtual Brain Models (SBI-VBMs),
5 and demonstrate that training deep neural networks on both spatio-temporal and
6 functional features allows for accurate estimation of generative parameters in brain disorders.
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13 Ref: [[https:~~/~~/iopscience.iop.org/article/10.1088/2632-2153/ad6230>>https://iopscience.iop.org/article/10.1088/2632-2153/ad6230]]