Institute of Developmental Biology and Neurobiology, Johannes-Gutenberg University Mainz, Mainz, Germany.
The Friedrich Miescher Institute for Biomedical Research (FMI), Basel, Switzerland.
Nat Commun. 2024 Oct 3;15(1):8580. doi: 10.1038/s41467-024-52724-5.
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.
自然场景变化迅速，这对视觉处理的可靠性构成挑战。然而，人类和许多动物能够做出准确的视觉行为，而计算机视觉设备在快速变化的背景亮度下却难以应对。动物视觉是如何做到这一点的呢？在这里，我们揭示了果蝇中快速亮度增益控制的算法和机制，从而实现了稳定的视觉处理。我们确定了特定的中脑神经元作为亮度增益控制的部位，这些神经元将这种特性传递给了方向选择性细胞。该回路还涉及到广域神经元，这与计算预测相匹配，即在快速变化的光照条件下，局部空间池化驱动自然场景中的最优对比度处理。实验和理论都表明，通过除法归一化，空间上的亮度信号可以实现亮度增益控制。这个过程依赖于使用谷氨酸门控氯离子通道 GluClα 的分流抑制。我们的工作描述了果蝇如何在动态变化的自然场景中稳健地处理视觉信息，这是所有视觉系统都面临的共同挑战。
