Neural pathways and computations that achieve stable contrast processing tuned to natural scenes

Abstract

SummaryNatural scenes are highly dynamic, challenging the reliability of visual processing. Yet, humans and many animals perform accurate visual behaviors, whereas computer vision devices struggle with changing environments. How does animal vision achieve this? Here, we reveal the algorithms and mechanisms of rapid luminance gain control inDrosophila,resulting in stable visual processing. We identify the dendrites of specific third order neurons, Tm1 and Tm9, as the site of luminance gain control. The circuitry further involves neurons with wide-field properties, matching computational predictions that local spatial pooling can drive optimal contrast processing in natural scenes where light conditions change rapidly. Experiments and theory argue that a spatially pooled luminance signal achieves luminance gain control via divisive normalization. This process relies on shunting inhibition using the glutamate-gated chloride channel GluClα. Our work describes computationally, algorithmically, and mechanistically, how visual circuits robustly process visual information in dynamically changing, natural scenes.