Movie reconstruction from mouse visual cortex activity

Abstract

The ability to reconstruct imagery represented by the brain has the potential to give us an intuitive understanding of what the brain sees. Reconstruction of visual input from human fMRI data has garnered significant attention in recent years. Comparatively less focus has been directed towards vision reconstruction from single-cell recordings, despite its potential to provide a more direct measure of the information represented by the brain. Here, we achieve high-quality reconstructions of videos presented to mice, from the activity of neurons in their visual cortex. Using our method of video optimization via gradient descent through a state-of-the-art dynamic neural encoding model we reliably reconstruct 10-second movies at 30 Hz from two-photon calcium imaging data. We achieve a ≈ 2-fold increase in pixel-by-pixel correlation compared to previous reconstructions of static images from mouse V1, while also capturing temporal dynamics. We find that critical for high-quality reconstructions are the number of neurons in the dataset and the use of model ensembling.