A critical goal of vision is to detect changes in light intensity, even when these changes are blurred by the spatial resolution of the eye and the motion of the animal. Here we describe a recurrent neural circuit in Drosophila that compensates for blur and thereby selectively enhances the perceived contrast of moving edges. Using in vivo, two-photon voltage imaging, we measured the temporal response properties of L1 and L2, two cell types that receive direct synaptic input from photoreceptors. These neurons have biphasic responses to brief flashes of light, a hallmark of cells that encode changes in stimulus intensity. However, the second phase was often much larger than the first, creating an unusual temporal filter. Genetic dissection revealed that recurrent neural circuitry strongly shapes the second phase of the response, informing the structure of a dynamical model. By applying this model to moving natural images, we demonstrate that rather than veridically representing stimulus changes, this temporal processing strategy systematically enhances them, amplifying and sharpening responses. Comparing the measured responses of L2 to model predictions across both artificial and natural stimuli revealed that L2 tunes its properties as the model predicts in order to deblur images. Since this strategy is tunable to behavioral context, generalizable to any time-varying sensory input, and implementable with a common circuit motif, we propose that it could be broadly used to selectively enhance sharp and salient changes.

中文翻译：

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果蝇的循环神经回路消除视觉输入模糊

视觉的一个关键目标是检测光强度的变化，即使这些变化因眼睛的空间分辨率和动物的运动而变得模糊。在这里，我们描述了果蝇中的循环神经回路，它可以补偿模糊，从而选择性地增强移动边缘的感知对比度。使用体内双光子电压成像，我们测量了 L1 和 L2 这两种从光感受器接收直接突触输入的细胞类型的时间响应特性。这些神经元对短暂的闪光有双相反应，这是编码刺激强度变化的细胞的标志。然而，第二阶段通常比第一阶段大得多，形成了一个不寻常的时间过滤器。遗传解剖揭示了循环神经回路强烈地塑造了反应的第二阶段，从而告知了动力学模型的结构。通过将该模型应用于移动的自然图像，我们证明了这种时间处理策略不是真实地表示刺激变化，而是系统地增强它们，放大和锐化响应。将 L2 的测量响应与人工和自然刺激下的模型预测进行比较表明，L2 会根据模型预测调整其属性，以消除图像模糊。由于该策略可根据行为环境进行调节，可推广到任何时变的感觉输入，并且可通过常见的电路主题来实现，因此我们建议它可以广泛用于选择性地增强急剧和显着的变化。