Computational models are an essential tool for understanding the origin and functions of the topographic organisation of the primate visual system. Yet, vision is most commonly modelled by convolutional neural networks that ignore topography by learning identical features across space. Here, we overcome this limitation by developing All-Topographic Neural Networks (All-TNNs). Trained on visual input, several features of primate topography emerge in All-TNNs: smooth orientation maps and cortical magnification in their first layer, and category-selective areas in their final layer. In addition, we introduce a novel dataset of human spatial biases in object recognition, which enables us to directly link models to behaviour. We demonstrate that All-TNNs significantly better align with human behaviour than previous state-of-the-art convolutional models due to their topographic nature. All-TNNs thereby mark an important step forward in understanding the spatial organisation of the visual brain and how it mediates visual behaviour.

翻译：计算模型是理解灵长类视觉系统地形组织起源与功能的重要工具。然而，视觉通常由卷积神经网络建模，这类网络通过学习空间上的相同特征而忽略了地形特性。在此，我们通过开发全地形神经网络（All-Topographic Neural Networks, All-TNNs）克服了这一局限。在视觉输入训练下，All-TNNs中涌现出灵长类地形的若干特征：第一层的平滑朝向图与皮层放大率，以及最终层的类别选择性区域。此外，我们引入了一个关于物体识别中人类空间偏好的新型数据集，从而能够将模型与行为直接关联。我们证明，由于其地形特性，All-TNNs与人类行为的对齐程度显著优于先前最先进的卷积模型。因此，All-TNNs标志着在理解视觉脑的空间组织及其如何介导视觉行为方面迈出了重要一步。