Last modified by mhashemi on 2024/12/03 18:26
From version 15.1
edited by mhashemi
on 2024/11/27 18:08
Change comment: There is no comment for this version
To version 10.1
edited by mhashemi
on 2024/11/27 18:06
Change comment: There is no comment for this version

Summary

Details

Page properties
Content
... ... @@ -1,14 +1,12 @@
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: }}}
8 8  
9 9  
10 -
11 -Code: [[https:~~/~~/github.com/ins-amu/SBI-VBMs>>https://github.com/ins-amu/SBI-VBMs]]
12 -
13 13  (% style="text-align: justify;" %)
14 14  Ref: [[https:~~/~~/iopscience.iop.org/article/10.1088/2632-2153/ad6230>>https://iopscience.iop.org/article/10.1088/2632-2153/ad6230]]