Department of Ophthalmology and Visual Sciences, John Moran Eye Center, University of Utah, Salt Lake City, Utah, USA; email:
Annu Rev Vis Sci. 2024 Sep;10(1):263-291. doi: 10.1146/annurev-vision-102122-110414.
The retina is an ideal model for understanding the fundamental rules for how neural networks are constructed. The compact neural networks of the retina perform all of the initial processing of visual information before transmission to higher visual centers in the brain. The field of retinal connectomics uses high-resolution electron microscopy datasets to map the intricate organization of these networks and further our understanding of how these computations are performed by revealing the fundamental topologies and allowable networks behind retinal computations. In this article, we review some of the notable advances that retinal connectomics has provided in our understanding of the specific cells and the organization of their connectivities within the retina, as well as how these are shaped in development and break down in disease. Using these anatomical maps to inform modeling has been, and will continue to be, instrumental in understanding how the retina processes visual signals.
视网膜是理解神经网络构建基本规则的理想模型。视网膜的紧凑神经网络在将视觉信息传输到大脑中的高级视觉中枢之前,完成了所有初始处理。视网膜连接组学领域使用高分辨率电子显微镜数据集来绘制这些网络的错综复杂的组织结构,并通过揭示视网膜计算背后的基本拓扑结构和允许的网络,进一步了解这些计算是如何进行的。在本文中,我们回顾了视网膜连接组学在理解特定细胞及其在视网膜内连接的组织方面所提供的一些显著进展,以及这些细胞在发育过程中是如何形成的,以及在疾病中是如何破坏的。使用这些解剖图谱来为建模提供信息,对于理解视网膜如何处理视觉信号一直是而且将继续是至关重要的。