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Temporal Adaptation Enhances Efficient Contrast Gain Control on Natural Images

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  • Fabian Sinz
  • Matthias Bethge

Abstract

Divisive normalization in primary visual cortex has been linked to adaptation to natural image statistics in accordance to Barlow's redundancy reduction hypothesis. Using recent advances in natural image modeling, we show that the previously studied static model of divisive normalization is rather inefficient in reducing local contrast correlations, but that a simple temporal contrast adaptation mechanism of the half-saturation constant can substantially increase its efficiency. Our findings reveal the experimentally observed temporal dynamics of divisive normalization to be critical for redundancy reduction. Author Summary: The redundancy reduction hypothesis postulates that neural representations adapt to sensory input statistics such that their responses become as statistically independent as possible. Based on this hypothesis, many properties of early visual neurons—like orientation selectivity or divisive normalization—have been linked to natural image statistics. Divisive normalization, in particular, models a widely observed neural response property: The divisive inhibition of a single neuron by a pool of others. This mechanism has been shown to reduce the redundancy among neural responses to typical contrast dependencies in natural images. Here, we show that the standard model of divisive normalization achieves substantially less redundancy reduction than a theoretically optimal mechanism called radial factorization. On the other hand, we find that radial factorization is inconsistent with existing neurophysiological observations. As a solution we suggest a new physiologically plausible modification of the standard model which accounts for the dynamics of the visual input by adapting to local contrasts during fixations. In this way the dynamic version of the standard model achieves almost optimal redundancy reduction performance. Our results imply that the dynamics of natural viewing conditions are critical for testing the role of divisive normalization for redundancy reduction.

Suggested Citation

  • Fabian Sinz & Matthias Bethge, 2013. "Temporal Adaptation Enhances Efficient Contrast Gain Control on Natural Images," PLOS Computational Biology, Public Library of Science, vol. 9(1), pages 1-13, January.
  • Handle: RePEc:plo:pcbi00:1002889
    DOI: 10.1371/journal.pcbi.1002889
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    References listed on IDEAS

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    1. Jan Eichhorn & Fabian Sinz & Matthias Bethge, 2009. "Natural Image Coding in V1: How Much Use Is Orientation Selectivity?," PLOS Computational Biology, Public Library of Science, vol. 5(4), pages 1-16, April.
    2. Sinz, Fabian & Gerwinn, Sebastian & Bethge, Matthias, 2009. "Characterization of the p-generalized normal distribution," Journal of Multivariate Analysis, Elsevier, vol. 100(5), pages 817-820, May.
    3. Goodman, Irwin R. & Kotz, Samuel, 1973. "Multivariate [theta]-generalized normal distributions," Journal of Multivariate Analysis, Elsevier, vol. 3(2), pages 204-219, June.
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    1. Ryan Webb & Paul W. Glimcher & Kenway Louie, 2021. "The Normalization of Consumer Valuations: Context-Dependent Preferences from Neurobiological Constraints," Management Science, INFORMS, vol. 67(1), pages 93-125, January.
    2. Landry, Peter & Webb, Ryan, 2021. "Pairwise normalization: A neuroeconomic theory of multi-attribute choice," Journal of Economic Theory, Elsevier, vol. 193(C).

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