Inferring depth from binocular disparities is a difficult problem for the visual system because local features in the left- and right-eye images must be matched correctly to solve this “stereo correspondence problem.” Cortical architecture and computational studies suggest that lateral interactions among neurons could help resolve local uncertainty about disparity encoded in individual neurons by incorporating contextual constraints. We found that correlated activity among pairs of neurons in primary visual cortex depended both on disparity-tuning relationships and the stimuli displayed within the receptive fields of the neurons. Nearby pairs of neurons with distinct disparity tuning exhibited a decrease in spike correlation at competing disparities soon after response onset. Distant neuronal pairs of similar disparity tuning exhibited an increase in spike correlation at mutually preferred disparities. The observed correlated activity and response dynamics suggests that local competitive and distant cooperative interactions improve disparity tuning of individual neurons over time. Such interactions could represent a neural substrate for the principal constraints underlying cooperative stereo algorithms.

In spite of the recent surge in the popularity of statistical approaches to vision, the joint statistics of coregistered range and light-intensity images have gone relatively unexplored. We investigate statistical correlations between images and the surface shapes that produced them. We determine which linear properties of range images can be best predicted from simple computations on intensity information, and we determine those properties of intensity images that best predict range information. We find that significant (up to ϭ 0.45) and potentially exploitable correlations exist between linear properties of range and intensity images, and we explore the structure of these correlations.

Abstract

Disparity tuning measured in the primary visual cortex (V1) is described well by the disparity energy model, but not all aspects of disparity tuning are fully explained by the model. Such deviations from the disparity energy model provide us with insight into how network interactions may play a role in disparity processing and help to solve the stereo correspondence problem. Here, we propose a neuronal circuit model with recurrent connections that provides a simple account of the observed deviations. The model is based on recurrent connections inferred from neurophysiological observations on spike timing correlations, and is in good accord with existing data on disparity tuning dynamics. We further performed two additional experiments to test predictions of the model. First, we increased the size of stimuli to drive more neurons and provide a stronger recurrent input. Our model predicted sharper disparity tuning for larger stimuli. Second, we displayed anti-correlated stereograms, where dots of opposite luminance polarity are matched between the left- and right-eye images and result in inverted disparity tuning in the disparity energy model. In this case, our model predicted reduced sharpening and strength of inverted disparity tuning. For both experiments, the dynamics of disparity tuning observed from the neurophysiological recordings in macaque V1 matched model simulation predictions. Overall, the results of this study support the notion that, while the disparity energy model provides a primary account of disparity tuning in V1 neurons, neural disparity processing in V1 neurons is refined by recurrent interactions among elements in the neural circuit.