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1		-{{{Virtual Brain Models imply latent nonlinear state space models
	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
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
	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
6	6	functional features allows for accurate estimation of generative parameters in brain disorders.
7	7
8		-Code: }}}
9	9
	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]]
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]]
	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	+}}}