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edited by mhashemi
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edited by mhashemi
on 2024/11/27 18:06
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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 -machine learning techniques for widely applicable Bayesian estimation.
3 +machinelearning 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 -}}}
8 +Code: }}}
9 9  
10 10  
11 -
12 -Code: [[https:~~/~~/github.com/ins-amu/SBI-VBMs>>https://github.com/ins-amu/SBI-VBMs]]
13 -
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]]