Sensory systems need to reliably extract information from highly variable natural signals. Flies, for instance, use optic flow to guide their course and are remarkably adept at estimating image velocity regardless of image statistics. Current circuit models, however, cannot account for this robustness. Here, we demonstrate that the Drosophila visual system reduces input variability by rapidly adjusting its sensitivity to local contrast conditions. We exhaustively map functional properties of neurons in the motion detection circuit and find that local responses are compressed by surround contrast. The compressive signal is fast, integrates spatially, and derives from neural feedback. Training convolutional neural networks on estimating the velocity of natural stimuli shows that this dynamic signal compression can close the performance gap between model and organism. Overall, our work represents a comprehensive mechanistic account of how neural systems attain the robustness to carry out survival-critical tasks in challenging real-world environments.

中文翻译：

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动态信号压缩可实现苍蝇的强大运动视觉。

感觉系统需要从高度可变的自然信号中可靠地提取信息。举例来说，苍蝇利用光流来引导它们的路线，并且无论图像统计如何，它们都非常擅长估计图像速度。但是，当前的电路模型无法说明这种鲁棒性。在这里，我们证明果蝇视觉系统通过快速调整其对局部对比度条件的敏感度来减少输入的可变性。我们详尽地绘制了运动检测电路中神经元的功能特性，并发现局部响应被周围对比度压缩。压缩信号快速，在空间上积分并从神经反馈中得出。训练卷积神经网络以估计自然刺激的速度表明，这种动态信号压缩可以弥合模型与生物之间的性能差距。总体而言，我们的工作代表了对神经系统如何在具有挑战性的现实环境中执行生存关键任务的鲁棒性的全面机制解释。