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

Burak Gür, Luisa Ramirez, Jacqueline Cornean, Freya Thurn, Sebastian Molina-Obando, Giordano Ramos-Traslosheros and Marion Silies ()
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Burak Gür: Johannes-Gutenberg University Mainz
Luisa Ramirez: Johannes-Gutenberg University Mainz
Jacqueline Cornean: Johannes-Gutenberg University Mainz
Freya Thurn: Johannes-Gutenberg University Mainz
Sebastian Molina-Obando: Johannes-Gutenberg University Mainz
Giordano Ramos-Traslosheros: Johannes-Gutenberg University Mainz
Marion Silies: Johannes-Gutenberg University Mainz

Nature Communications, 2024, vol. 15, issue 1, 1-18

Abstract: Abstract Natural 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 rapidly changing background luminance. How does animal vision achieve this? Here, we reveal the algorithms and mechanisms of rapid luminance gain control in Drosophila, resulting in stable visual processing. We identify specific transmedullary neurons as the site of luminance gain control, which pass this property to direction-selective cells. The circuitry further involves wide-field neurons, matching computational predictions that local spatial pooling drive optimal contrast processing in natural scenes when 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 how the fly robustly processes visual information in dynamically changing natural scenes, a common challenge of all visual systems.

Date: 2024
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DOI: 10.1038/s41467-024-52724-5

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