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1		-This tool was developed at INS in Marseille.
2		-Authors: M Hashemi, A Ziaeemehr, MM Woodman, J Fousek, S Petkoski, VK Jirsa
3		-
4		-Virtual Brain Models imply latent nonlinear state space models
	1	+{{{Virtual Brain Models imply latent nonlinear state space models
5	5	driven by noise and network input, necessitating advanced probabilistic
6		-machine learning techniques for widely applicable Bayesian estimation.
7		-Here we present Simulation-based Inference on Virtual Brain Models (SBI-VBMs),
8		-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
9	9	functional features allows for accurate estimation of generative parameters in brain disorders.
10	10
11	11
12		-Code: [[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/ebrains-task-3-3/Drive#notebooks/SBI-VBM>>https://wiki.ebrains.eu/bin/view/Collabs/ebrains-task-3-3/Drive#notebooks/SBI-VBM]]
	9	+Code: }}}
13	13
	11	+
14	14	(% style="text-align: justify;" %)
15	15	Ref: [[https:~~/~~/iopscience.iop.org/article/10.1088/2632-2153/ad6230>>https://iopscience.iop.org/article/10.1088/2632-2153/ad6230]]
16		-
17		-{{{
18		-@article{SBI-VBM,
19		- title={Simulation-based inference on virtual brain models of disorders},
20		- author={Hashemi, Meysam and Ziaeemehr, Abolfazl and Woodman, Marmaduke M and Fousek, Jan and Petkoski, Spase and Jirsa, Viktor K},
21		- journal={Machine Learning: Science and Technology},
22		- volume={5},
23		- number={3},
24		- pages={035019},
25		- year={2024},
26		- publisher={IOP Publishing}
27		-}
28		-}}}