Ideal binocular disparity detectors learned using independent subspace analysis on binocular natural image pairs
Date
16/03/2016Funder
Grant ID
DGB1500
Metadata
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Abstract
An influential theory of mammalian vision, known as the efficient coding hypothesis, holds that early stages in the visual cortex attempts to form an efficient coding of ecologically valid stimuli. Although numerous authors have successfully modelled some aspects of early vision mathematically, closer inspection has found substantial discrepancies between the predictions of some of these models and observations of neurons in the visual cortex. In particular analysis of linear-non-linear models of simple-cells using Independent Component Analysis has found a strong bias towards features on the horoptor. In order to investigate the link between the information content of binocular images, mathematical models of complex cells and physiological recordings, we applied Independent Subspace Analysis to binocular image patches in order to learn a set of complex-cell-like models. We found that these complex-cell-like models exhibited a wide range of binocular disparity-discriminability, although only a minority exhibited high binocular discrimination scores. However, in common with the linear-non-linear model case we found that feature detection was limited to the horoptor suggesting that current mathematical models are limited in their ability to explain the functionality of the visual cortex.
Citation
Hunter , D W & Hibbard , P B 2016 , ' Ideal binocular disparity detectors learned using independent subspace analysis on binocular natural image pairs ' , PLoS One , vol. 11 , no. 3 , e0150117 . https://doi.org/10.1371/journal.pone.0150117
Publication
PLoS One
Status
Peer reviewed
ISSN
1932-6203Type
Journal article
Rights
Copyright: © 2016 Hunter, Hibbard. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Description
This work was funded by the Biotechnology and Biological Sciences Research Council (BBSRC) grant [BB/K018973/1].Collections
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