Trial-by-trial reliability of responses in the primary visual cortex on binocular disparity depends on stimulus order

2013 ◽  
Vol 37 (9) ◽  
pp. 1487-1500
Author(s):  
Vasily Vorobyov
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Binocular Disparity ◽  
Stimulus Order

scholarly journals Near-optimal combination of disparity across a log-polar scaled visual field

2019 ◽  
Author(s):  
Guido Maiello ◽  
Manuela Chessa ◽  
Peter J. Bex ◽  
Fabio Solari
Keyword(s):  
Visual Cortex ◽  
Visual Field ◽  
Visual System ◽  
Primary Visual Cortex ◽  
Depth Perception ◽  
Binocular Disparity ◽  
Spatial Scales ◽  
Visual Input ◽  
Depth Information ◽  
Central Vision

AbstractThe human visual system is foveated: we can see fine spatial details in central vision, whereas resolution is poor in our peripheral visual field, and this loss of resolution follows an approximately logarithmic decrease. Additionally, our brain organizes visual input in polar coordinates. Therefore, the image projection occurring between retina and primary visual cortex can be mathematically described by the log-polar transform. Here, we test and model how this space-variant visual processing affects how we process binocular disparity, a key component of human depth perception. We observe that the fovea preferentially processes disparities at fine spatial scales, whereas the visual periphery is tuned for coarse spatial scales, in line with the naturally occurring distributions of depths and disparities in the real-world. We further show that the visual field integrates disparity information across the visual field, in a near-optimal fashion. We develop a foveated, log-polar model that mimics the processing of depth information in primary visual cortex and that can process disparity directly in the cortical domain representation. This model takes real images as input and recreates the observed topography of disparity sensitivity in man. Our findings support the notion that our foveated, binocular visual system has been moulded by the statistics of our visual environment.Author summaryWe investigate how humans perceive depth from binocular disparity at different spatial scales and across different regions of the visual field. We show that small changes in disparity-defined depth are detected best in central vision, whereas peripheral vision best captures the coarser structure of the environment. We also demonstrate that depth information extracted from different regions of the visual field is combined into a unified depth percept. We then construct an image-computable model of disparity processing that takes into account how our brain organizes the visual input at our retinae. The model operates directly in cortical image space, and neatly accounts for human depth perception across the visual field.

scholarly journals Testing Quantitative Models of Binocular Disparity Selectivity in Primary Visual Cortex

2003 ◽  
Vol 90 (5) ◽  
pp. 2795-2817 ◽  
Author(s):  
Jenny C. A. Read ◽  
Bruce G. Cumming
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Binocular Vision ◽  
Striate Cortex ◽  
Binocular Disparity ◽  
Energy Model ◽  
Critical Assessment ◽  
V1 Neurons ◽  
Initial Processing ◽  
Made In

Disparity-selective neurons in striate cortex (V1) probably implement the initial processing that supports binocular vision. Recently, much progress has been made in understanding the computations that these neurons perform on retinal inputs. The binocular energy model has been highly successful in providing a simple theory of these computations. A key feature of the energy model is that it is linear until after inputs from the two eyes are combined. Recently, however, a modified version of the energy model, incorporating threshold nonlinearities before binocular combination, has been proposed to account for the weaker disparity tuning observed with anticorrelated stimuli. In this study, we present new data needed for a critical assessment of these two models. We compare two key predictions of the models with responses of disparity-selective neurons recorded from V1 of awake fixating monkeys. We find that the original energy model, and a family of generalizations retaining linear binocular combination, are quantitatively inconsistent with the response of V1 neurons. In contrast, the modified version incorporating threshold nonlinearities can explain both sets of observations. We conclude that the energy model can be reconciled with experimental observations by adding a threshold before binocular combination. This gives us the clearest picture yet of the computation being carried out by disparity-selective V1 neurons.

scholarly journals Ocular Dominance Predicts Neither Strength Nor Class of Disparity Selectivity With Random-Dot Stimuli in Primate V1

2004 ◽  
Vol 91 (3) ◽  
pp. 1271-1281 ◽  
Author(s):  
Jenny C. A. Read ◽  
Bruce G. Cumming
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Binocular Disparity ◽  
Tuning Curve ◽  
Ocular Dominance ◽  
Experimental Studies ◽  
Theoretical Models ◽  
Tuning Curves ◽  
Inhibitory Type ◽  
Random Dot Patterns

We address two unresolved issues concerning the coding of binocular disparity in primary visual cortex. Experimental studies and theoretical models have suggested a relationship between a cell's ocular dominance, assessed with monocular stimuli, and its tuning to binocular disparity. First, the disparity energy model of disparity selectivity suggests that there should be a correlation between ocular dominance and the strength of disparity tuning. Second, several studies have reported a relationship between ocular dominance and the shape of the disparity tuning curve, with cells dominated by one eye more likely to have disparity tuning of the tuned-inhibitory type. We investigated both of these relationships in single neurons recorded from the primary visual cortex of awake fixating macaques, using dynamic random-dot patterns as a stimulus. To classify disparity tuning curves quantitatively, we develop a new measure of symmetry, which can be applied to any function. We find no evidence for any correlation between ocular dominance and the nature of disparity tuning. This places constraints on the circuitry underlying disparity tuning.

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