Unsupervised Learning of Cone Spectral Classes from Natural Images

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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.

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Benson, N. C., Manning, J. R., & Brainard, D. H. (2014). Unsupervised Learning of Cone Spectral Classes from Natural Images. PLoS Computational Biology, 10(6). https://doi.org/10.1371/journal.pcbi.1003652

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