Information Flow in Biological Networks for Color Vision
Biological neural networks for color vision (also known as color appearance models) consist of a cascade of linear + nonlinear layers that modify the linear measurements at the retinal photo-receptors leading to an internal (nonlinear) representation of color that correlates with psychophysical expe...
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MDPI AG
2022-10-01
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| Online Access: | https://www.mdpi.com/1099-4300/24/10/1442 |
| _version_ | 1797473457782390784 |
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| author | Jesús Malo |
| author_facet | Jesús Malo |
| author_sort | Jesús Malo |
| collection | DOAJ |
| description | Biological neural networks for color vision (also known as color appearance models) consist of a cascade of linear + nonlinear layers that modify the linear measurements at the retinal photo-receptors leading to an internal (nonlinear) representation of color that correlates with psychophysical experience. The basic layers of these networks include: (1) chromatic adaptation (normalization of the mean and covariance of the color manifold); (2) change to opponent color channels (PCA-like rotation in the color space); and (3) saturating nonlinearities to obtain perceptually Euclidean color representations (similar to dimension-wise equalization). The <i>Efficient Coding Hypothesis</i> argues that these transforms should emerge from information-theoretic goals. In case this hypothesis holds in color vision, the question is <i>what is the coding gain due to the different layers of the color appearance networks</i>? In this work, a representative family of color appearance models is analyzed in terms of how the redundancy among the chromatic components is modified along the network and how much information is transferred from the input data to the noisy response. The proposed analysis is performed using data and methods that were not available before: (1) new colorimetrically calibrated scenes in different CIE illuminations for the proper evaluation of chromatic adaptation; and (2) new statistical tools to estimate (multivariate) information-theoretic quantities between multidimensional sets based on Gaussianization. The results confirm that the efficient coding hypothesis holds for current color vision models, and identify the psychophysical mechanisms critically responsible for gains in information transference: opponent channels and their nonlinear nature are more important than chromatic adaptation at the retina. |
| first_indexed | 2024-03-09T20:14:46Z |
| format | Article |
| id | doaj.art-a16811e8e03d41c8a0ffe739581a5c72 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T20:14:46Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
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| spelling | doaj.art-a16811e8e03d41c8a0ffe739581a5c722023-11-24T00:03:51ZengMDPI AGEntropy1099-43002022-10-012410144210.3390/e24101442Information Flow in Biological Networks for Color VisionJesús Malo0Image Processing Lab, Universitat de Valencia, 46980 Valencia, SpainBiological neural networks for color vision (also known as color appearance models) consist of a cascade of linear + nonlinear layers that modify the linear measurements at the retinal photo-receptors leading to an internal (nonlinear) representation of color that correlates with psychophysical experience. The basic layers of these networks include: (1) chromatic adaptation (normalization of the mean and covariance of the color manifold); (2) change to opponent color channels (PCA-like rotation in the color space); and (3) saturating nonlinearities to obtain perceptually Euclidean color representations (similar to dimension-wise equalization). The <i>Efficient Coding Hypothesis</i> argues that these transforms should emerge from information-theoretic goals. In case this hypothesis holds in color vision, the question is <i>what is the coding gain due to the different layers of the color appearance networks</i>? In this work, a representative family of color appearance models is analyzed in terms of how the redundancy among the chromatic components is modified along the network and how much information is transferred from the input data to the noisy response. The proposed analysis is performed using data and methods that were not available before: (1) new colorimetrically calibrated scenes in different CIE illuminations for the proper evaluation of chromatic adaptation; and (2) new statistical tools to estimate (multivariate) information-theoretic quantities between multidimensional sets based on Gaussianization. The results confirm that the efficient coding hypothesis holds for current color vision models, and identify the psychophysical mechanisms critically responsible for gains in information transference: opponent channels and their nonlinear nature are more important than chromatic adaptation at the retina.https://www.mdpi.com/1099-4300/24/10/1442chromatic informationcolor appearance networksefficient coding hypothesistotal correlationmutual informationGaussianization |
| spellingShingle | Jesús Malo Information Flow in Biological Networks for Color Vision Entropy chromatic information color appearance networks efficient coding hypothesis total correlation mutual information Gaussianization |
| title | Information Flow in Biological Networks for Color Vision |
| title_full | Information Flow in Biological Networks for Color Vision |
| title_fullStr | Information Flow in Biological Networks for Color Vision |
| title_full_unstemmed | Information Flow in Biological Networks for Color Vision |
| title_short | Information Flow in Biological Networks for Color Vision |
| title_sort | information flow in biological networks for color vision |
| topic | chromatic information color appearance networks efficient coding hypothesis total correlation mutual information Gaussianization |
| url | https://www.mdpi.com/1099-4300/24/10/1442 |
| work_keys_str_mv | AT jesusmalo informationflowinbiologicalnetworksforcolorvision |