Benson Noah C, Manning Jeremy R, Brainard David H
Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America; Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
PLoS Comput Biol. 2014 Jun 26;10(6):e1003652. doi: 10.1371/journal.pcbi.1003652. eCollection 2014 Jun.
The first step in the evolution of primate trichromatic color vision was the expression of a third cone class not present in ancestral mammals. This observation motivates a fundamental question about the evolution of any sensory system: how is it possible to detect and exploit the presence of a novel sensory class? We explore this question in the context of primate color vision. We present an unsupervised learning algorithm capable of both detecting the number of spectral cone classes in a retinal mosaic and learning the class of each cone using the inter-cone correlations obtained in response to natural image input. The algorithm's ability to classify cones is in broad agreement with experimental evidence about functional color vision for a wide range of mosaic parameters, including those characterizing dichromacy, typical trichromacy, anomalous trichromacy, and possible tetrachromacy.
灵长类动物三色视觉进化的第一步是表达出一种在远古哺乳动物中不存在的第三类视锥细胞。这一观察引发了关于任何感觉系统进化的一个基本问题：如何能够检测并利用新感觉类别的存在？我们在灵长类动物颜色视觉的背景下探讨这个问题。我们提出一种无监督学习算法，它既能检测视网膜镶嵌图中光谱视锥细胞类别的数量，又能利用响应自然图像输入而获得的视锥细胞间相关性来学习每个视锥细胞的类别。对于广泛的镶嵌图参数，包括那些表征二色性、典型三色性、异常三色性以及可能的四色性的参数，该算法对视锥细胞进行分类的能力与关于功能性颜色视觉的实验证据大致相符。
