Abstract
The sequential analysis of information in a coarse-to-fine (CtF) manner is a fundamental processing strategy of the visual system. Previous studies have shown that neurons in the primary visual cortex (V1) of anesthetized animals can process spatial information in a CtF fashion, shifting their spatial frequency (SF) preference from low (coarse) to high (fine) throughout their response to static grating stimuli. However, many central questions regarding CtF processing, such as whether it occurs in awake behaving mice and potential computational advantages it may provide, remain unexplored. Here, we performed large-scale single unit recordings to characterize CtF processing in both anesthetized and awake mice, determine its developmental profile, and study its role in encoding ethologically relevant natural scenes. Using high-density multielectrode silicon probes and subspace mapping of receptive fields, we found that the vast majority of V1 neurons from awake adult mice displayed two temporally discrete peaks in their spatiotemporal receptive field, each with distinct SF preferences. The SF shift between these 2 peaks was large and nearly always from low to high (i.e. CtF). Additionally, we discovered CtF processing is significantly attenuated in anesthetized mice and develops postnatally via experience-dependent mechanisms. Finally, we show that awake mice process the complex spatial statistics of natural scenes in a CtF manner. Excitingly, we demonstrate that this CtF processing reduces redundancy in the neural representation of natural scenes by shifting the population response away from the high-power, low-SF statistical regularities in these stimuli. This redundancy reduction drove an increase in the representational efficiency of natural images that did not occur in anesthetized or dark-reared mice with significantly attenuated CtF processing. Collectively, these findings establish a novel, state-dependent, computation of cortical circuitry that develops after vision onset to allow the animal to efficiently encode the complex spatial statistics of natural scenes.