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.

翻译：大脑活动的基础模型通过模拟跨任务和模态下对复杂刺激的神经反应，为计算神经科学开辟了新前沿。一个自然的后续问题是：这些模型能否反向应用？即能否从合成大脑活动中恢复刺激本身或其属性？我们在一个概念验证场景中，使用TRIBEv2模型研究了这一问题。我们将大脑仿真器与大型语言模型（LLMs）配对，后者可根据情感效价、唤醒度和优势度等语言参数生成新闻标题。随后，我们采用基于仿真的推断来学习从大脑图谱到潜在刺激参数的概率映射。结果表明，这些参数可从预测的大脑图谱中恢复，验证了神经编码的质量。同时，LLMs可作为可控刺激生成器用于模拟实验。这些发现为基于基础大脑模型的解码与逆向设计迈出了关键一步。