V1T: large-scale mouse V1 response prediction using a Vision Transformer
This work addresses the problem of accurate neural response prediction in computational neuroscience, offering a new benchmark for researchers studying the visual cortex, though it is incremental as it builds on existing Vision Transformer methods.
The paper tackled the challenge of predicting mouse primary visual cortex neural responses to natural visual stimuli by introducing V1T, a Vision Transformer-based architecture, and achieved over 12.7% improvement in prediction performance compared to previous convolution-based models on large datasets.
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.