小柯机器人

研究揭示视网膜感受野的马赛克间协调
2021-03-14 20:48

美国杜克大学Greg D. Field研究团队揭示视网膜感受野的马赛克间协调。相关论文于2021年3月10日在线发表于国际学术期刊《自然》。

研究人员表示,视网膜的输出被组织成许多检测网格,称为“马赛克”,它们将视觉场景的不同特征传递给大脑。每个马赛克都包含一种类型的视网膜神经节细胞(RGC),其感受野可平铺视觉空间。许多马赛克成对出现,分别表示特定视觉特征的信号增量(ON)和减量(OFF)。

研究人员使用有效编码模型来确定应如何安排此类马赛克对,从而优化自然场景的编码。研究人员发现,当这些马赛克对进行反对齐时,信息将最大化,这意味着不同马赛克的感受野中心之间的距离大于偶然的预期。研究人员使用大鼠和灵长类RGC的光响应大规模测量结果,在多个感受野马赛克上测试了这一预测。具有相似特征选择性的ON和OFF RGC配对具有反对准的感受野马赛克图,与该预测一致。编码不同特征的ON和OFF RGC类型具有独立的马赛克。

这些结果将有效的编码理论扩展到单个细胞之外,从而可以预测各种类型的RGC的群体如何在空间上排列。 

附:英文原文

Title: Inter-mosaic coordination of retinal receptive fields

Author: Suva Roy, Na Young Jun, Emily L. Davis, John Pearson, Greg D. Field

Issue&Volume: 2021-03-10

Abstract: The output of the retina is organized into many detector grids, called ‘mosaics’, that signal different features of visual scenes to the brain1,2,3,4. Each mosaic comprises a single type of retinal ganglion cell (RGC), whose receptive fields tile visual space. Many mosaics arise as pairs, signalling increments (ON) and decrements (OFF), respectively, of a particular visual feature5. Here we use a model of efficient coding6 to determine how such mosaic pairs should be arranged to optimize the encoding of natural scenes. We find that information is maximized when these mosaic pairs are anti-aligned, meaning that the distances between the receptive field centres across mosaics are greater than expected by chance. We tested this prediction across multiple receptive field mosaics acquired using large-scale measurements of the light responses of rat and primate RGCs. ON and OFF RGC pairs with similar feature selectivity had anti-aligned receptive field mosaics, consistent with this prediction. ON and OFF RGC types that encode distinct features have independent mosaics. These results extend efficient coding theory beyond individual cells to predict how populations of diverse types of RGC are spatially arranged.

DOI: 10.1038/s41586-021-03317-5

Source: https://www.nature.com/articles/s41586-021-03317-5

 

Nature:《自然》,创刊于1869年。隶属于施普林格·自然出版集团,最新IF:43.07
官方网址:http://www.nature.com/
投稿链接:http://www.nature.com/authors/submit_manuscript.html


本期文章:《自然》:Online/在线发表

分享到:

0