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

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.

Semantic scene descriptions as an objective of human vision

Interpreting the meaning of a visual scene requires not only identification of its constituent objects, but also a rich semantic characterization of object interrelations. Here, we study the neural mechanisms underlying visuo-semantic transformations by applying modern computational techniques to a large-scale 7T fMRI dataset of human brain responses elicited by complex natural scenes. Using semantic embeddings obtained by applying linguistic deep learning models to human-generated scene descriptions, we identify a widely distributed network of brain regions that encode semantic scene descriptions. Importantly, these semantic embeddings better explain activity in these regions than traditional object category labels. In addition, they are effective predictors of activity despite the fact that the participants did not actively engage in a semantic task, suggesting that visuo-semantic transformations are a default mode of vision. In support of this view, we then show that highly accurate reconstructions of scene captions can be directly linearly decoded from patterns of brain activity. Finally, a recurrent convolutional neural network trained on semantic embeddings further outperforms semantic embeddings in predicting brain activity, providing a mechanistic model of the brain's visuo-semantic transformations. Together, these experimental and computational results suggest that transforming visual input into rich semantic scene descriptions may be a central objective of the visual system, and that focusing efforts on this new objective may lead to improved models of visual information processing in the human brain.

Recurrence required to capture the dynamic computations of the human ventral visual stream

The visual system is an intricate network of brain regions that enables us to recognize the world around us. Despite its abundant lateral and feedback connections, human object processing is commonly viewed and studied as a feedforward process. Here, we measure and model the rapid representational dynamics across multiple stages of the human ventral stream using time-resolved brain imaging and deep learning. We observe substantial representational transformations during the first 300 ms of processing within and across ventral-stream regions. Categorical divisions emerge in sequence, cascading forward and in reverse across regions, and Granger causality analysis suggests bidirectional information flow between regions. Finally, recurrent deep neural network models clearly outperform feedforward models in terms of their ability to jointly capture the multi-region cortical dynamics. These results establish that recurrent models are required to understand information processing in the human ventral stream.