Summary

• Page properties (1 modified, 0 added, 0 removed)

Details

Page properties

Content

...	...	@@ -1,11 +1,11 @@
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		-Code: }}}
	8	+Code: }}}
9	9
10	10
11	11	(% style="text-align: justify;" %)