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Accurate predictive models of the visual cortex neural response to naturalvisual stimuli remain a challenge in computational neuroscience. In this work,we introduce V1T, a novel Vision Transformer based architecture that learns ashared visual and behavioral representation across animals. We evaluate ourmodel on two large datasets recorded from mouse primary visual cortex andoutperform previous convolution-based models by more than 12.7% in predictionperformance. Moreover, we show that the self-attention weights learned by theTransformer correlate with the population receptive fields. Our model thus setsa new benchmark for neural response prediction and can be used jointly withbehavioral and neural recordings to reveal meaningful characteristic featuresof the visual cortex.

V1T: large-scale mouse V1 response prediction using a Vision Transformer