Inverting Foundation Models of Brain Function with Simulation-Based Inference
For computational neuroscience, this proof-of-concept shows that foundation brain models can be inverted for decoding, though the approach is incremental and limited to synthetic data.
This work demonstrates that latent stimulus parameters (e.g., valence, arousal) can be recovered from synthetic brain activity generated by a foundation model (TRIBEv2) using simulation-based inference, validating neural encoding quality and enabling controllable stimulus generation with LLMs.
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 parameters can be recovered from predicted brain maps, validating the quality of neural encodings. They also show that LLMs can serve as controllable stimulus generators for simulated experiments. Together, these findings provide a step toward decoding and inverse design with foundation brain models.