Literature DB >> 10444685

Neural mechanisms for processing binocular information I. Simple cells.

A Anzai1, I Ohzawa, R D Freeman.   

Abstract

The visual system integrates information from the left and right eyes and constructs a visual world that is perceived as single and three dimensional. To understand neural mechanisms underlying this process, it is important to learn about how signals from the two eyes interact at the level of single neurons. Using a sophisticated receptive field (RF) mapping technique that employs binary m-sequences, we have determined the rules of binocular interactions exhibited by simple cells in the cat's striate cortex in relation to the structure of their monocular RFs. We find that binocular interaction RFs of most simple cells are well described as the product of left and right eye RFs. Therefore the binocular interactions depend not only on binocular disparity but also on monocular stimulus position or phase. The binocular interaction RF is consistent with that predicted by a model of a linear binocular filter followed by a static nonlinearity. The static nonlinearity is shown to have a shape of a half-power function with an average exponent of approximately 2. Although the initial binocular convergence of signals is linear, the static nonlinearity makes binocular interaction multiplicative at the output of simple cells. This multiplicative binocular interaction is a key ingredient for the computation of interocular cross-correlation, an algorithm for solving the stereo correspondence problem. Therefore simple cells may perform initial computations necessary to solve this problem.

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Mesh:

Year:  1999        PMID: 10444685     DOI: 10.1152/jn.1999.82.2.891

Source DB:  PubMed          Journal:  J Neurophysiol        ISSN: 0022-3077            Impact factor:   2.714


  48 in total

1.  How simple cells are made in a nonlinear network model of the visual cortex.

Authors:  D J Wielaard; M Shelley; D McLaughlin; R Shapley
Journal:  J Neurosci       Date:  2001-07-15       Impact factor: 6.167

2.  Nonlinear spectrotemporal sound analysis by neurons in the auditory midbrain.

Authors:  Monty A Escabi; Christoph E Schreiner
Journal:  J Neurosci       Date:  2002-05-15       Impact factor: 6.167

Review 3.  Neural computations underlying depth perception.

Authors:  Akiyuki Anzai; Gregory C DeAngelis
Journal:  Curr Opin Neurobiol       Date:  2010-05-06       Impact factor: 6.627

4.  The role of the posterior parietal cortex in stereopsis and hand-eye coordination during motor task behaviours.

Authors:  Giulia Paggetti; Daniel Richard Leff; Felipe Orihuela-Espina; George Mylonas; Ara Darzi; Guang-Zhong Yang; Gloria Menegaz
Journal:  Cogn Process       Date:  2014-11-14

5.  Ocular dominance predicts neither strength nor class of disparity selectivity with random-dot stimuli in primate V1.

Authors:  Jenny C A Read; Bruce G Cumming
Journal:  J Neurophysiol       Date:  2003-10-01       Impact factor: 2.714

6.  Computational subunits of visual cortical neurons revealed by artificial neural networks.

Authors:  Brian Lau; Garrett B Stanley; Yang Dan
Journal:  Proc Natl Acad Sci U S A       Date:  2002-06-11       Impact factor: 11.205

7.  Testing quantitative models of binocular disparity selectivity in primary visual cortex.

Authors:  Jenny C A Read; Bruce G Cumming
Journal:  J Neurophysiol       Date:  2003-07-16       Impact factor: 2.714

Review 8.  Mapping receptive fields in primary visual cortex.

Authors:  Dario L Ringach
Journal:  J Physiol       Date:  2004-05-21       Impact factor: 5.182

9.  Untuned suppression makes a major contribution to the enhancement of orientation selectivity in macaque v1.

Authors:  Dajun Xing; Dario L Ringach; Michael J Hawken; Robert M Shapley
Journal:  J Neurosci       Date:  2011-11-02       Impact factor: 6.167

10.  The accuracy of membrane potential reconstruction based on spiking receptive fields.

Authors:  Deepankar Mohanty; Benjamin Scholl; Nicholas J Priebe
Journal:  J Neurophysiol       Date:  2012-01-25       Impact factor: 2.714

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