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