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.

翻译：大脑皮层中邻近的神经元共享相似的反应特征，在感觉和认知系统中产生系统的空间组织。近期地形模型重现了这种结构的某些方面，但仍局限于单模态处理，并对各层独立施加空间约束，导致生成的图谱既无法捕捉皮层处理流的连续性，也无法体现跨模态整合。我们提出Topo-Omni——一种地形多模态模型，其中视觉、听觉及语言/认知处理共享单一的连续硅片表征。通过以空间平滑性目标微调预训练基础模型，该架构发展出与人类神经影像学一致的跨模态聚类，覆盖从感觉到认知系统。驱动或抑制特定聚类能选择性偏移或损害感知能力，这与人类干预研究的结果相吻合。最后，我们利用该模型在硅片中筛选新型聚类，并发现自然景观与动物网络等新特征——这些发现已在人类数据中得到验证。因此，单一空间原则即可组织跨模态和处理阶段的表征，为皮层组织提供可检验的假说。