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What Is the Limit of Redundancy Reduction with Divisive Normalization?

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84226

Sinz,  F
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83805

Bethge,  M
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Sinz, F., & Bethge, M. (2013). What Is the Limit of Redundancy Reduction with Divisive Normalization? Neural Computation, 25(11), 2809-2814. doi:10.1162/NECO_a_00505.


Cite as: http://hdl.handle.net/11858/00-001M-0000-001A-12D6-A
Abstract
Divisive normalization has been proposed as a nonlinear redundancy reduction mechanism capturing contrast correlations. Its basic function is a radial rescaling of the population response. Because of the saturation of divisive normalization, however, it is impossible to achieve a fully independent representation. In this letter, we derive an analytical upper bound on the inevitable residual redundancy of any saturating radial rescaling mechanism.