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

Bryan M. Li; Isabel M. Cornacchia; Nathalie L. Rochefort; Arno Onken

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arXiv · 2302.03023

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

Bryan M. Li·Isabel M. Cornacchia·Nathalie L. Rochefort·Arno Onken

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Abstract

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.

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BibTeX RIS

Bryan M. Li, Isabel M. Cornacchia, Nathalie L. Rochefort, Arno Onken. 2023-02-06. V1T: large-scale mouse V1 response prediction using a Vision Transformer. https://arxiv.org/abs/2302.03023

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