Unsupervised Learning of Cone Spectral Classes from Natural Images

Overview

Journal PLoS Comput Biol

Specialty Biology

Date 2014 Jun 27

PMID 24967877

Citations 12

Authors

Noah C Benson

Jeremy R Manning

David H Brainard

Affiliations

Soon will be listed here.

Abstract

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.

Citing Articles

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