Abstract
By examining the experimental data on the statistical properties of natural scenes together with (retinal) contrast sensitivity data, we arrive at a first principle, theoretical hypothesis for the purpose of retinal processing and its relationship to an animal's environment. We argue that the retinal goal is to transform the visual input as much as possible into a statistically independent basis as the first step in creating a redundancy reduced representation in the cortex, as suggested by Barlow. The extent of this whitening of the input is limited, however, by the need to suppress input noise. Our explicit theoretical solutions for the retinal filters also show a simple dependence on mean stimulus luminance: they predict an approximate Weber law at low spatial frequencies and a De Vries-Rose law at high frequencies. Assuming that the dominant source of noise is quantum, we generate a family of contrast sensitivity curves as a function of mean luminance. This family is compared to psychophysical data.
Keywords
LuminanceContrast (vision)Artificial intelligenceStimulus (psychology)Computer scienceSpatial frequencyVisual cortexAlgorithmRetinalRedundancy (engineering)PsychophysicsPattern recognition (psychology)MathematicsComputer visionPsychologyOpticsPerceptionPhysicsCognitive psychologyNeuroscience
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Publication Info
- Year
- 1992
- Type
- article
- Volume
- 4
- Issue
- 2
- Pages
- 196-210
- Citations
- 732
- Access
- Closed
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Cite This
Joseph J. Atick,
Amanda Redlich
(1992).
What Does the Retina Know about Natural Scenes?.
Neural Computation
, 4
(2)
, 196-210.
https://doi.org/10.1162/neco.1992.4.2.196
Identifiers
- DOI
- 10.1162/neco.1992.4.2.196