Psychophysically Tuned Divisive Normalization factorizes the PDF of Natural Images
View Researcher II's Other CodesDisclaimer: “The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).”
Please contact us in case of a broken link from here
Authors | J. Malo & V. Laparra |
Journal/Conference Name | Neural Computation |
Paper Category | ECE |
Paper Abstract | 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. |
Date of publication | 2010 |
Code Programming Language | MATLAB |
Comment |