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1		-{{{Virtual Brain Models imply latent nonlinear state space models
	1	+Virtual Brain Models imply latent nonlinear state space models
2	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
	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	6	functional features allows for accurate estimation of generative parameters in brain disorders.
7	7
8		-Code: }}}
9	9
	9	+Code: [[https:~~/~~/github.com/ins-amu/SBI-VBMs>>https://github.com/ins-amu/SBI-VBMs]]
10	10
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]]