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Computational models are an essential tool for understanding the origin andfunctions of the topographic organisation of the primate visual system. Yet,vision is most commonly modelled by convolutional neural networks that ignoretopography by learning identical features across space. Here, we overcome thislimitation by developing All-Topographic Neural Networks (All-TNNs). Trained onvisual input, several features of primate topography emerge in All-TNNs: smoothorientation maps and cortical magnification in their first layer, andcategory-selective areas in their final layer. In addition, we introduce anovel dataset of human spatial biases in object recognition, which enables usto directly link models to behaviour. We demonstrate that All-TNNssignificantly better align with human behaviour than previous state-of-the-artconvolutional models due to their topographic nature. All-TNNs thereby mark animportant step forward in understanding the spatial organisation of the visualbrain and how it mediates visual behaviour.

End-to-end topographic networks as models of cortical map formation and human visual behaviour: moving beyond convolutions