Inverting Foundation Models of Brain Function with Simulation-Based Inference

Niels Leif Bracher, Xavier Intes, Stefan T. Radev

Keywords: simulation-based inference, amortized inference, neural emulators, foundation models, probabilistic decoding

TL;DR: We present a proof-of-concept for inverting foundation brain models with simulation-based inference, recovering latent linguistic parameters from synthetic TRIBEv2 brain responses.

Abstract:

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.

Email Sharing: We authorize the sharing of all author emails with Program Chairs.

Data Release: We authorize the release of our submission and author names to the public in the event of acceptance.

Submission Number: 24