  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.
