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  A new class of distributions for natural images generalizing independent subspace analysis

Sinz, F., & Bethge, M. (2009). A new class of distributions for natural images generalizing independent subspace analysis. Poster presented at Bernstein Conference on Computational Neuroscience (BCCN 2009), Frankfurt a.M., Germany. doi:10.3389/conf.neuro.10.2009.14.127.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C298-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-0C91-C
Genre: Poster

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 Creators:
Sinz, F1, 2, Author              
Bethge, M1, 2, Author              
Affiliations:
1Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: The Redundancy Reduction Hypothesis by Barlow and Attneave suggests a link between the statistics of natural images and the physiologically observed structure and function in the early visual system. In particular, algorithms and probabilistic models like Independent Component Analysis, Independent Subspace Analysis and Radial Factorization, which allow for redundancy reduction mechanism, have been used successfully to generate several features of the early visual system such as bandpass filtering, contrast gain control, and orientation selective filtering when applied to natural images. Here, we propose a new family of probability distributions, called Lp-nested symmetric distributions, that comprises all of the above algorithms for natural images. This general class of distributions allows us to quantitatively asses (i) how well the assumptions made by all of the redundancy reducing models are justified for natural images, (ii) how large the contribution of each of these mechanisms (shape of filters, non-linear contrast gain control, subdivision into subspace) to redundancy reduction is. For ISA, we find that partitioning the space into different subspace only yields a competitive model when applied after contrast gain control. In this case, however, we find that the single filter responses are already almost independent. Therefore, we conclude that a partitioning into subspaces does not considerably improve the model which makes band-pass filtering (whitening) and contrast gain control (divisive normalization) the two most important mechanisms.

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 Dates: 2009-08
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.3389/conf.neuro.10.2009.14.127
BibTex Citekey: 5966
 Degree: -

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Title: Bernstein Conference on Computational Neuroscience (BCCN 2009)
Place of Event: Frankfurt a.M., Germany
Start-/End Date: 2009-09-30 - 2009-10-02

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Title: Frontiers in Computational Neuroscience
  Abbreviation : Front Comput Neurosci
Source Genre: Journal
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Publ. Info: Lausanne : Frontiers Research Foundation
Pages: - Volume / Issue: 2009 (Conference Abstract: Bernstein Conference on Computational Neuroscience) Sequence Number: - Start / End Page: 114 - 115 Identifier: Other: 1662-5188
CoNE: https://pure.mpg.de/cone/journals/resource/1662-5188