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Vertical Binocular Disparity is Encoded Implicitly within a Model Neuronal Population Tuned to Horizontal Disparity and Orientation

Lookup NU author(s): Professor Jenny Read

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Abstract

Primary visual cortex is often viewed as a "cyclopean retina'', performing the initial encoding of binocular disparities between left and right images. Because the eyes are set apart horizontally in the head, binocular disparities are predominantly horizontal. Yet, especially in the visual periphery, a range of non-zero vertical disparities do occur and can influence perception. It has therefore been assumed that primary visual cortex must contain neurons tuned to a range of vertical disparities. Here, I show that this is not necessarily the case. Many disparity-selective neurons are most sensitive to changes in disparity orthogonal to their preferred orientation. That is, the disparity tuning surfaces, mapping their response to different two-dimensional (2D) disparities, are elongated along the cell's preferred orientation. Because of this, even if a neuron's optimal 2D disparity has zero vertical component, the neuron will still respond best to a non-zero vertical disparity when probed with a sub-optimal horizontal disparity. This property can be used to decode 2D disparity, even allowing for realistic levels of neuronal noise. Even if all V1 neurons at a particular retinotopic location are tuned to the expected vertical disparity there (for example, zero at the fovea), the brain could still decode the magnitude and sign of departures from that expected value. This provides an intriguing counter-example to the common wisdom that, in order for a neuronal population to encode a quantity, its members must be tuned to a range of values of that quantity. It demonstrates that populations of disparity-selective neurons encode much richer information than previously appreciated. It suggests a possible strategy for the brain to extract rarely-occurring stimulus values, while concentrating neuronal resources on the most commonly-occurring situations.


Publication metadata

Author(s): Read JCA

Publication type: Article

Publication status: Published

Journal: PLoS Computational Biology

Year: 2010

Volume: 6

Issue: 4

Print publication date: 01/04/2010

Date deposited: 09/07/2010

ISSN (print): 1553-734X

ISSN (electronic): 1553-7358

Publisher: Public Library of Science

URL: http://dx.doi.org/10.1371/journal.pcbi.1000754

DOI: 10.1371/journal.pcbi.1000754


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