Neurons in Primate Area MSTd Signal Eye Movement Direction Inferred from Dynamic Perspective Cues in Optic Flow.

Smooth eye movements are common during natural viewing; we frequently rotate our eyes to track moving objects or to maintain fixation on an object during self-movement. Reliable information about smooth eye movements is crucial to various neural computations, such as estimating heading from optic flow or judging depth from motion parallax. While it is well established that extraretinal signals (e.g., efference copies of motor commands) carry critical information about eye velocity, the rotational optic flow field produced by eye rotations also carries valuable information. Although previous work has shown that dynamic perspective cues in optic flow can be used in computations that require estimates of eye velocity, it has remained unclear where and how the brain processes these visual cues and how they are integrated with extraretinal signals regarding eye rotation. We examined how neurons in the dorsal region of the medial superior temporal area (MSTd) of two male rhesus monkeys represent the direction of smooth pursuit eye movements based on both visual cues (dynamic perspective) and extraretinal signals. We find that most MSTd neurons have matched preferences for the direction of eye rotation based on visual and extraretinal signals. Moreover, neural responses to combinations of these signals are well predicted by a weighted linear summation model. These findings demonstrate a neural substrate for representing the velocity of smooth eye movements based on rotational optic flow and establish area MSTd as a key node for integrating visual and extraretinal signals into a more generalized representation of smooth eye movements.SIGNIFICANCE STATEMENT We frequently rotate our eyes to smoothly track objects of interest during self-motion. Information about eye velocity is crucial for a variety of computations performed by the brain, including depth perception and heading perception. Traditionally, information about eye rotation has been thought to arise mainly from extraretinal signals, such as efference copies of motor commands. Previous work shows that eye velocity can also be inferred from rotational optic flow that accompanies smooth eye movements, but the neural origins of these visual signals about eye rotation have remained unknown. We demonstrate that macaque neurons signal the direction of smooth eye rotation based on visual signals, and that they integrate both visual and extraretinal signals regarding eye rotation in a congruent fashion.

Flexible coding of object motion in multiple reference frames by parietal cortex neurons.

Neurons represent spatial information in diverse reference frames, but it remains unclear whether neural reference frames change with task demands and whether these changes can account for behavior. In this study, we examined how neurons represent the direction of a moving object during self-motion, while monkeys switched, from trial to trial, between reporting object direction in head- and world-centered reference frames. Self-motion information is needed to compute object motion in world coordinates but should be ignored when judging object motion in head coordinates. Neural responses in the ventral intraparietal area are modulated by the task reference frame, such that population activity represents object direction in either reference frame. In contrast, responses in the lateral portion of the medial superior temporal area primarily represent object motion in head coordinates. Our findings demonstrate a neural representation of object motion that changes with task requirements.

The Effects of Population Tuning and Trial-by-Trial Variability on Information Encoding and Behavior.

Identifying the features of population responses that are relevant to the amount of information encoded by neuronal populations is a crucial step toward understanding population coding. Statistical features, such as tuning properties, individual and shared response variability, and global activity modulations, could all affect the amount of information encoded and modulate behavioral performance. We show that two features in particular affect information: the modulation of population responses across conditions (population signal) and the inverse population covariability along the modulation axis (projected precision). We demonstrate that fluctuations of these two quantities are correlated with fluctuations of behavioral performance in various tasks and brain regions consistently across 4 monkeys (1 female and 1 male Macaca mulatta; and 2 male Macaca fascicularis). In contrast, fluctuations in mean correlations among neurons and global activity have negligible or inconsistent effects on the amount of information encoded and behavioral performance. We also show that differential correlations reduce the amount of information encoded in finite populations by reducing projected precision. Our results are consistent with predictions of a model that optimally decodes population responses to produce behavior.SIGNIFICANCE STATEMENT The last two or three decades of research have seen hot debates about what features of population tuning and trial-by-trial variability influence the information carried by a population of neurons, with some camps arguing, for instance, that mean pairwise correlations or global fluctuations are important while other camps report opposite results. In this study, we identify the most important features of neural population responses that determine the amount of encoded information and behavioral performance by combining analytic calculations with a novel nonparametric method that allows us to isolate the effects of different statistical features. We tested our hypothesis on 4 macaques, three decision-making tasks, and two brain areas. The predictions of our theory were in agreement with the experimental data.

Coding of stereoscopic depth information in visual areas V3 and V3A.

The process of stereoscopic depth perception is thought to begin with the analysis of absolute binocular disparity, the difference in position of corresponding features in the left and right eye images with respect to the points of fixation. Our sensitivity to depth, however, is greater when depth judgments are based on relative disparity, the difference between two absolute disparities, compared to when they are based on absolute disparity. Therefore, the visual system is thought to compute relative disparities for fine depth discrimination. Functional magnetic resonance imaging studies in humans and monkeys have suggested that visual areas V3 and V3A may be specialized for stereoscopic depth processing based on relative disparities. In this study, we measured absolute and relative disparity-tuning of neurons in V3 and V3A of alert fixating monkeys, and we compared their basic tuning properties with those published previously for other visual areas. We found that neurons in V3 and V3A predominantly encode absolute, not relative, disparities. We also found that basic parameters of disparity-tuning in V3 and V3A are similar to those from other extrastriate visual areas. Finally, by comparing single-unit activity with multi-unit activity measured at the same recording site, we demonstrate that neurons with similar disparity selectivity are clustered in both V3 and V3A. We conclude that areas V3 and V3A are not particularly specialized for processing stereoscopic depth information compared to other early visual areas, at least with respect to the tuning properties that we have examined.

Neural computations underlying depth perception.

Neural mechanisms underlying depth perception are reviewed with respect to three computational goals: determining surface depth order, gauging depth intervals, and representing 3D surface geometry and object shape. Accumulating evidence suggests that these three computational steps correspond to different stages of cortical processing. Early visual areas appear to be involved in depth ordering, while depth intervals, expressed in terms of relative disparities, are likely represented at intermediate stages. Finally, 3D surfaces appear to be processed in higher cortical areas, including an area in which individual neurons encode 3D surface geometry, and a population of these neurons may therefore represent 3D object shape. How these processes are integrated to form a coherent 3D percept of the world remains to be understood.

Neurons in monkey visual area V2 encode combinations of orientations.

Contours and textures are important attributes of object surfaces, and are often described by combinations of local orientations in visual images. To elucidate the neural mechanisms underlying contour and texture processing, we examined receptive field (RF) structures of neurons in visual area V2 of the macaque monkey for encoding combinations of orientations. By measuring orientation tuning at several locations within the classical RF, we found that a majority (70%) of V2 neurons have similar orientation tuning throughout the RF. However, many others have RFs containing subregions tuned to different orientations, most commonly about 90 degrees apart. By measuring interactions between two positions within the RF, we found that approximately one-third of neurons show inhibitory interactions that make them selective for combinations of orientations. These results indicate that V2 neurons could play an important role in analyzing contours and textures and could provide useful cues for surface segmentation.