Inverting Foundation Models of Brain Function with Simulation-Based Inference

2026-04-26

AI Summary

This study investigates the reverse decoding of semantic attributes—such as valence, arousal, and dominance—of original stimuli from synthetic brain activity signals. For the first time, a large language model (LLM) is employed as a controllable stimulus generator, integrated with the TRIBEv2 brain foundation model and simulation-based inference to establish a probabilistic mapping from predicted neural representations to latent stimulus parameters. The proposed approach not only successfully recovers the semantic properties of the stimuli, thereby validating the fidelity of the underlying brain encoding model, but also demonstrates the novel potential of LLMs in simulating neuroscientific experiments and enabling inverse design in neuroscience.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Humans and AI: Brain-Sensing and Analysis
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• inverse decoding
• foundation brain models
• stimulus reconstruction
• neural encoding
• simulation-based inference

Innovation

• simulation-based inference
• foundation models
• brain decoding
• large language models
• inverse design