Visual systems are often equipped with neurons that detect small moving objects, which may represent prey, predators, or conspecifics. Although the processing properties of those neurons have been studied in diverse organisms, links between the proposed algorithms and animal behaviors or circuit mechanisms remain elusive. Here, we have investigated behavioral function, computational algorithm, and neurochemical mechanisms of an object-selective neuron, LC11, in Drosophila. With genetic silencing and optogenetic activation, we show that LC11 is necessary for a visual object-induced stopping behavior in walking flies, a form of short-term freezing, and its activity can promote stopping. We propose a new quantitative model for small object selectivity based on the physiology and anatomy of LC11 and its inputs. The model accurately reproduces LC11 responses by pooling fast-adapting, tightly size-tuned inputs. Direct visualization of neurotransmitter inputs to LC11 confirmed the model conjectures about upstream processing. Our results demonstrate how adaptation can enhance selectivity for behaviorally relevant, dynamic visual features.

视觉系统通常配备有检测小型移动物体的神经元，这些物体可能代表猎物、捕食者或同种动物。尽管这些神经元的处理特性已经在不同的生物体中进行了研究，但所提出的算法与动物行为或电路机制之间的联系仍然难以捉摸。在这里，我们研究了果蝇中对象选择神经元 LC11 的行为功能、计算算法和神经化学机制。通过基因沉默和光遗传学激活，我们发现 LC11 对于行走果蝇的视觉物体诱导的停止行为（一种短期冻结的形式）是必需的，并且其活性可以促进停止。我们基于 LC11 的生理学和解剖学及其输入，提出了一种新的小物体选择性定量模型。该模型通过汇集快速适应、尺寸严格调整的输入，准确地再现 LC11 响应。LC11 神经递质输入的直接可视化证实了有关上游处理的模型猜想。我们的结果证明了适应如何增强对行为相关的动态视觉特征的选择性。