Inverting Foundation Models of Brain Function with Simulation-Based Inference
Foundation models of brain activity promise a new frontier for in silico neuroscience by emulating neural responses to complex stimuli across tasks and modalities. A natural next step is to ask whether these models can also be used in reverse. Can we recover a stimulus or its properties from synthetic brain activity? We study this question in a proof-of-concept setting using TRIBEv2. We pair the brain emulator with large language models (LLMs) that generate news headlines from linguistic parameters such as valence, arousal, and dominance. We then use simulation-based inference to learn a probabilistic mapping from brain maps to latent stimulus parameters. Our results show that these param...
| Authors | Niels Leif Bracher, Xavier Intes, Stefan T. Radev |
|---|---|
| Primary category | cs.LG |
| Published | 26 April 2026 |
| Last updated at source | 28 August 2026 |
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