Deep probabilistic model synthesis enables unified modeling of whole-brain neural activity across individual subjects
Many disciplines need quantitative models that synthesize experimental data across multiple instances of the same general system. For example, neuroscientists must combine data from the brains of many individual animals to understand the species' brain in general. However, typical machine learning models treat one system instance at a time. Here we introduce a machine learning framework, deep probabilistic model synthesis (DPMS), that leverages system properties auxiliary to the model to combine data across system instances. DPMS specifically uses variational inference to learn a conditional prior distribution and instance-specific posterior distributions over model parameters that respec...
| Authors | William E. Bishop, Luuk W. Hesselink, Bernhard Englitz et al. |
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| Primary category | cs.LG |
| Published | 15 March 2026 |
| Last updated at source | 13 October 2026 |
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