"What Not" Detectors Help the Brain See in Depth
Publication Date
2017-05-22Journal Title
Current Biology
ISSN
0960-9822
Publisher
Elsevier (Cell Press)
Volume
27
Issue
10
Pages
1403-1412
Language
English
Type
Article
This Version
VoR
Metadata
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Reis Goncalves, N., & Welchman, A. (2017). "What Not" Detectors Help the Brain See in Depth. Current Biology, 27 (10), 1403-1412. https://doi.org/10.1016/j.cub.2017.03.074
Abstract
Binocular stereopsis is one of the primary cues for three-dimensional (3D) vision in species ranging from insects to primates. Understanding how the brain extracts depth from two different retinal images represents a tractable challenge in sensory neuroscience that has so far evaded full explanation. Central to current thinking is the idea that the brain needs to identify matching features in the two retinal images (i.e., solving the "stereoscopic correspondence problem") so that the depth of objects in the world can be triangulated. Although intuitive, this approach fails to account for key physiological and perceptual observations. We show that formulating the problem to identify "correct matches" is suboptimal and propose an alternative, based on optimal information encoding, that mixes disparity detection with "proscription": exploiting dissimilar features to provide evidence against unlikely interpretations. We demonstrate the role of these "what not" responses in a neural network optimized to extract depth in natural images. The network combines information for and against the likely depth structure of the viewed scene, naturally reproducing key characteristics of both neural responses and perceptual interpretations. We capture the encoding and readout computations of the network in simple analytical form and derive a binocular likelihood model that provides a unified account of long-standing puzzles in 3D vision at the physiological and perceptual levels. We suggest that marrying detection with proscription provides an effective coding strategy for sensory estimation that may be useful for diverse feature domains (e.g., motion) and multisensory integration.
Keywords
3D vision, binocular disparity, convolutional neural network, da Vinci stereopsis, depth perception, wallpaper illusion
Sponsorship
Wellcome Trust ( 095183/Z/10/Z ).
Funder references
Wellcome Trust (095183/Z/10/Z)
Embargo Lift Date
2100-01-01
Identifiers
External DOI: https://doi.org/10.1016/j.cub.2017.03.074
This record's URL: https://www.repository.cam.ac.uk/handle/1810/264999
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