神经科学和AI交叉领域的研究者会感兴趣——Topo-Omni用单一模型统一了多模态皮层地图，还能预测新脑区，做认知建模或脑启发AI的团队值得关注。
研究团队提出 Topo-Omni，一种多模态地形模型，将视觉、听觉和语言/认知处理整合到单一连续的模拟皮层上。该模型通过微调预训练基础模型并加入空间平滑约束，自发形成了与人类神经影像一致的跨模态功能簇。通过驱动或抑制特定簇，可以选择性影响感知，模拟人类干预实验。模型还发现了新的自然景观和动物网络功能簇，并在人类数据中得到验证。这项工作表明单一空间原则即可组织跨模态和跨处理阶段的表征，为皮层组织提供可检验的假设。
Discovering Functionally Selective Brain Regions with a Deep Topographic Multimodal Model
Nearby neurons in cortex share similar response profiles, producing systematic spatial organization across sensory and cognitive systems. Recent topographic models reproduce aspects of this structure but remain unimodal and spatially constrain each layer separately, yielding fragmented maps that capture neither the contiguity of cortical processing streams nor their integration across modalities. We introduce Topo-Omni, a topographic multimodal model in which visual, auditory, and language/cognitive processing share a single contiguous in-silico sheet. Built by fine-tuning a pretrained foundation model with a spatial smoothness objective, this architecture develops clusters across modalities that are consistent with human neuroimaging, from sensory to cognitive systems. Driving or suppressing a cluster selectively biases or impairs perception, paralleling human intervention studies. Finally, we use our model to screen for novel clusters in-silico and discover new natural landscape and animal networks which we validate in human data. A single spatial principle thus organizes representations across modalities and processing stages, yielding testable hypotheses about cortical organization.