Literature DB >> 19761330

A neurophysiologically plausible population code model for human contrast discrimination.

Robbe L T Goris1, Felix A Wichmann, G Bruce Henning.   

Abstract

The pedestal effect is the improvement in the detectability of a sinusoidal grating in the presence of another grating of the same orientation, spatial frequency, and phase-usually called the pedestal. Recent evidence has demonstrated that the pedestal effect is differently modified by spectrally flat and notch-filtered noise: The pedestal effect is reduced in flat noise but virtually disappears in the presence of notched noise (G. B. Henning & F. A. Wichmann, 2007). Here we consider a network consisting of units whose contrast response functions resemble those of the cortical cells believed to underlie human pattern vision and demonstrate that, when the outputs of multiple units are combined by simple weighted summation-a heuristic decision rule that resembles optimal information combination and produces a contrast-dependent weighting profile-the network produces contrast-discrimination data consistent with psychophysical observations: The pedestal effect is present without noise, reduced in broadband noise, but almost disappears in notched noise. These findings follow naturally from the normalization model of simple cells in primary visual cortex, followed by response-based pooling, and suggest that in processing even low-contrast sinusoidal gratings, the visual system may combine information across neurons tuned to different spatial frequencies and orientations.

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Year:  2009        PMID: 19761330     DOI: 10.1167/9.7.15

Source DB:  PubMed          Journal:  J Vis        ISSN: 1534-7362            Impact factor:   2.240


  9 in total

Review 1.  Lateral effects in pattern vision.

Authors:  John M Foley
Journal:  J Vis       Date:  2019-08-01       Impact factor: 2.240

2.  Sensitivity to gaze-contingent contrast increments in naturalistic movies: An exploratory report and model comparison.

Authors:  Thomas S A Wallis; Michael Dorr; Peter J Bex
Journal:  J Vis       Date:  2015       Impact factor: 2.240

Review 3.  The divisive normalization model of V1 neurons: a comprehensive comparison of physiological data and model predictions.

Authors:  Tadamasa Sawada; Alexander A Petrov
Journal:  J Neurophysiol       Date:  2017-08-23       Impact factor: 2.714

4.  Invariance in visual object recognition requires training: a computational argument.

Authors:  Robbe L T Goris; Hans P Op de Beeck
Journal:  Front Neurosci       Date:  2010-04-15       Impact factor: 4.677

5.  Stochastic model for detection of signals in noise.

Authors:  Stanley A Klein; Dennis M Levi
Journal:  J Opt Soc Am A Opt Image Sci Vis       Date:  2009-11       Impact factor: 2.129

6.  Neural correlates of stimulus spatial frequency-dependent contrast detection.

Authors:  Jianjun Meng; Ruilong Liu; Ke Wang; Tianmiao Hua; Zhong-Lin Lu; Minmin Xi
Journal:  Exp Brain Res       Date:  2013-01-12       Impact factor: 1.972

7.  A new perceptual bias reveals suboptimal population decoding of sensory responses.

Authors:  Tom Putzeys; Matthias Bethge; Felix Wichmann; Johan Wagemans; Robbe Goris
Journal:  PLoS Comput Biol       Date:  2012-04-12       Impact factor: 4.475

8.  Apparent Motion Suppresses Responses in Early Visual Cortex: A Population Code Model.

Authors:  Nathalie Van Humbeeck; Tom Putzeys; Johan Wagemans
Journal:  PLoS Comput Biol       Date:  2016-10-26       Impact factor: 4.475

9.  What is the primary cause of individual differences in contrast sensitivity?

Authors:  Daniel H Baker
Journal:  PLoS One       Date:  2013-07-26       Impact factor: 3.240

  9 in total

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