Wiki source code of Simulation-based Inference on Virtual Brain Models of Disorders (SBI-VBMs)
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14.2 | 1 | Virtual Brain Models imply latent nonlinear state space models |
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9.1 | 2 | driven by noise and network input, necessitating advanced probabilistic |
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14.2 | 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 | ||
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9.1 | 6 | functional features allows for accurate estimation of generative parameters in brain disorders. |
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13.1 | 10 | |
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13.2 | 11 | Code: [[https:~~/~~/github.com/ins-amu/SBI-VBMs>>https://github.com/ins-amu/SBI-VBMs]] |
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8.1 | 13 | (% style="text-align: justify;" %) |
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7.1 | 14 | Ref: [[https:~~/~~/iopscience.iop.org/article/10.1088/2632-2153/ad6230>>https://iopscience.iop.org/article/10.1088/2632-2153/ad6230]] |