Efficient coding of natural images in the mouse visual cortex

Abstract

How communication between neurons gives rise to natural vision remains a matter of intense investigation. The mid-level visual areas along the ventral stream, as studies in primates have shown, are selective to a common class of natural images—textures—but a circuit-level understanding of this selectivity and its link to perception remain unclear. We addressed these questions in mice, first showing that they can perceptually discriminate between texture types and statistically simpler spectrally matched stimuli. Then, at the neural level, we found that the secondary visual area (LM), more than the primary one (V1), was selective for the higher-order statistics of textures, both at the mesoscopic and single-cell levels. At the circuit level, textures were encoded in neural activity subspaces whose relative distances correlated with the statistical complexity of the images and with the mice’s ability to discriminate between them. These dependencies were more significant in LM, in which the texture-related subspaces were smaller and closer to each other, enabling better stimulus decoding in this area. Together, our results demonstrate texture vision in mice, finding a linking framework between stimulus statistics, neural representations, and perceptual sensitivity—a distinct hallmark of efficient coding computations.