Wiki source code of Simulation-based Inference on Virtual Brain Models of Disorders (SBI-VBMs)
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9.1 | 1 | {{{Virtual Brain Models imply latent nonlinear state space models |
| 2 | driven by noise and network input, necessitating advanced probabilistic | ||
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10.1 | 3 | machine learning techniques for widely applicable Bayesian estimation. |
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9.1 | 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 | functional features allows for accurate estimation of generative parameters in brain disorders. | ||
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13.1 | 9 | Code: }}} |
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8.1 | 12 | (% style="text-align: justify;" %) |
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7.1 | 13 | Ref: [[https:~~/~~/iopscience.iop.org/article/10.1088/2632-2153/ad6230>>https://iopscience.iop.org/article/10.1088/2632-2153/ad6230]] |