Retinal circuits transform the pixel representation of photoreceptors into the feature representations of ganglion cells, whose axons transmit these representations to the brain. Functional, morphological, and transcriptomic surveys have identified more than 40 retinal ganglion cell (RGC) types in mice. RGCs extract features of varying complexity; some simply signal local differences in brightness (i.e., luminance contrast), whereas others detect specific motion trajectories. To understand the retina, we need to know how retinal circuits give rise to the diverse RGC feature representations. A catalog of the RGC feature set, in turn, is fundamental to understanding visual processing in the brain. Anterograde tracing indicates that RGCs innervate more than 50 areas in the mouse brain. Current maps connecting RGC types to brain areas are rudimentary, as is our understanding of how retinal signals are transformed downstream to guide behavior. In this article, I review the feature selectivities of mouse RGCs, how they arise, and how they are utilized downstream. Not only is knowledge of the behavioral purpose of RGC signals critical for understanding the retinal contributions to vision; it can also guide us to the most relevant areas of visual feature space.

中文翻译：

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视网膜神经节细胞的特征检测

视网膜电路将感光器的像素表示转换为神经节细胞的特征表示，神经节细胞的轴突将这些表示传输到大脑。功能、形态学和转录组学研究已在小鼠体内鉴定出 40 多种视网膜神经节细胞 (RGC) 类型。 RGC 提取不同复杂度的特征；有些只是发出亮度局部差异（即亮度对比度）的信号，而另一些则检测特定的运动轨迹。为了了解视网膜，我们需要知道视网膜回路如何产生不同的 RGC 特征表示。反过来，RGC 特征集的目录对于理解大脑中的视觉处理至关重要。顺行追踪表明 RGC 支配小鼠大脑中 50 多个区域。目前将 RGC 类型与大脑区域连接起来的图谱还很初级，我们对视网膜信号如何向下游转化以指导行为的理解也是如此。在本文中，我回顾了小鼠 RGC 的特征选择性、它们是如何产生的以及它们如何在下游被利用。了解 RGC 信号的行为目的不仅对于理解视网膜对视力的贡献至关重要；它还可以引导我们到达视觉特征空间中最相关的区域。