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1	1	{{{Virtual Brain Models imply latent nonlinear state space models
2	2	driven by noise and network input, necessitating advanced probabilistic
3		-machinelearning techniques for widely applicable Bayesian estimation.
	3	+machine learning techniques for widely applicable Bayesian estimation.
4	4	Here we present Simulation-based Inference on Virtual Brain Models (SBI-VBMs),
5	5	and demonstrate that training deep neural networks on both spatio-temporal and
6	6	functional features allows for accurate estimation of generative parameters in brain disorders.
7	7
8		-Code: }}}
9	9
	9	+Code: }}}
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	11	+
11	11	(% style="text-align: justify;" %)
12	12	Ref: [[https:~~/~~/iopscience.iop.org/article/10.1088/2632-2153/ad6230>>https://iopscience.iop.org/article/10.1088/2632-2153/ad6230]]