6533b820fe1ef96bd1279b82
RESEARCH PRODUCT
Psychophysically Tuned Divisive Normalization Approximately Factorizes the PDF of Natural Images
Valero LaparraJesús Malosubject
NeuronsComputational neurosciencebusiness.industryCognitive Neurosciencemedia_common.quotation_subjectModels NeurologicalNormalization (image processing)Pattern recognitionMutual informationInformation theoryMachine learningcomputer.software_genreVisual processingModels of neural computationArts and Humanities (miscellaneous)PerceptionVisual PerceptionArtificial intelligenceEfficient coding hypothesisbusinesscomputerVisual Cortexmedia_commonMathematicsdescription
The conventional approach in computational neuroscience in favor of the efficient coding hypothesis goes from image statistics to perception. It has been argued that the behavior of the early stages of biological visual processing (e.g., spatial frequency analyzers and their nonlinearities) may be obtained from image samples and the efficient coding hypothesis using no psychophysical or physiological information. In this work we address the same issue in the opposite direction: from perception to image statistics. We show that psychophysically fitted image representation in V1 has appealing statistical properties, for example, approximate PDF factorization and substantial mutual information reduction, even though no statistical information is used to fit the V1 model. These results are complementary evidence in favor of the efficient coding hypothesis.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2010-12-01 | Neural Computation |