The superior colliculus (SC) is a prominent and conserved visual center in all vertebrates. In mice, the most superficial lamina of the SC is enriched with neurons that are selective for the moving direction of visual stimuli. Here we study how these direction selective neurons respond to complex motion patterns known as plaids, using two-photon calcium imaging in awake male and female mice. The plaid pattern consists of two superimposed sinusoidal gratings moving in different directions, giving an apparent pattern direction that lies between the directions of the two component gratings. Most direction selective neurons in the mouse SC respond robustly to the plaids and show a high selectivity for the moving direction of the plaid pattern but not of its components. Pattern motion selectivity is seen in both excitatory and inhibitory SC neurons and is especially prevalent in response to plaids with large cross angles between the two component gratings. However, retinal inputs to the SC are ambiguous in their selectivity to pattern versus component motion. Modeling suggests that pattern motion selectivity in the SC can arise from a nonlinear transformation of converging retinal inputs. In contrast, the prevalence of pattern motion selective neurons is not seen in the primary visual cortex (V1). These results demonstrate an interesting difference between the SC and V1 in motion processing and reveal the SC as an important site for encoding pattern motion.

The brain combines 2-dimensional images received from the two eyes to form a percept of 3-dimensional surroundings. This process of binocular integration in the primary visual cortex (V1) serves as a useful model for studying how neural circuits generate emergent properties from multiple input signals. Here, we perform a thorough characterization of binocular integration using electrophysiological recordings in the V1 of awake adult male and female mice, by systematically varying the orientation and phase disparity of monocular and binocular stimuli. We reveal widespread binocular integration in mouse V1 and demonstrate that the three commonly studied binocular properties – ocular dominance, interocular matching, and disparity selectivity – are independent from each other. For individual neurons, the responses to monocular stimulation can predict the average amplitude of binocular response, but not its selectivity. Finally, the extensive and independent binocular integration of monocular inputs is seen across cortical layers, in both regular-spiking and fast-spiking neurons, regardless of stimulus design. Our data indicate that the current model of simple feedforward convergence is inadequate to account for binocular integration in mouse V1, thus suggesting an indispensable role played by intracortical circuits in binocular computation.

Significance Statement Binocular integration is an important step of visual processing that takes place in the visual cortex. Studying the process by which V1 neurons become selective for certain binocular disparities is informative about how neural circuits integrate multiple information streams at a more general level. Here, we systematically characterize binocular integration in mice. Our data demonstrate more widespread and complex binocular integration in mouse V1 than previously reported. Binocular responses cannot be explained by a simple convergence of monocular responses, contrary to the prevailing model of binocular integration. These findings thus indicate that intracortical circuits must be involved in the exquisite computation of binocular disparity, which would endow brain circuits with the plasticity needed for binocular development and processing.

The brain creates a single visual percept of the world with inputs from two eyes. This means that downstream structures must integrate information from the two eyes coherently. Not only does the brain meet this challenge effortlessly, it also uses small differences between the two eyes’ inputs, i.e., binocular disparity, to construct depth information in a perceptual process called stereopsis. Recent studies have advanced our understanding of the neural circuits underlying stereoscopic vision and its development. Here, we review these advances in the context of three binocular properties that have been most commonly studied for visual cortical neurons: ocular dominance of response magnitude, interocular matching of orientation preference, and response selectivity for binocular disparity. By focusing mostly on mouse studies, as well as recent studies using ferrets and tree shrews, we highlight unresolved controversies and significant knowledge gaps regarding the neural circuits underlying binocular vision. We note that in most ocular dominance studies, only monocular stimulations are used, which could lead to a mischaracterization of binocularity. On the other hand, much remains unknown regarding the circuit basis of interocular matching and disparity selectivity and its development. We conclude by outlining opportunities for future studies on the neural circuits and functional development of binocular integration in the early visual system.

Despite its importance, the development of higher visual areas (HVAs) at the cellular resolution remains largely unknown. Here, we conducted 2-photon calcium imaging of mouse HVAs lateromedial (LM) and anterolateral (AL) and V1 to observe developmental changes in visual response properties. HVA neurons showed selectivity for orientations and directions similar to V1 neurons at eye opening, which became sharper in the following weeks. Neurons in all areas over all developmental stages tended to respond selectively to dots moving along an axis perpendicular to their preferred orientation at slow speeds, suggesting a certain level of conventional motion coding already at eye opening. In contrast, at high speeds, many neurons responded to dots moving along the axis parallel to the preferred orientation in older animals but rarely after eye opening, indicating a lack of motion-streak coding in the earlier stage. Together, our results uncover the development of visual properties in HVAs.

Decision-making is an essential cognitive process by which we interact with the external world. However, attempts to understand the neural mechanisms of decision-making are limited by the current available animal models and the technologies that can be applied to them. Here, we build on the renewed interest in using tree shrews (Tupaia Belangeri) in vision research and provide strong support for them as a model for studying visual perceptual decision-making. Tree shrews learned very quickly to perform a two-alternative forced choice contrast discrimination task, and they exhibited differences in response time distributions depending on the reward and punishment structure of the task. Specifically, they made occasional fast guesses when incorrect responses are punished by a constant increase in the interval between trials. This behavior was suppressed when faster incorrect responses were discouraged by longer inter-trial intervals. By fitting the behavioral data with two variants of racing diffusion decision models, we found that the between-trial delay affected decision-making by modulating the drift rate of a time accumulator. Our results thus provide support for the existence of an internal process that is independent of the evidence accumulation in decision-making and lay a foundation for future mechanistic studies of perceptual decision-making using tree shrews.

Neurons in the primary visual cortex (V1) are tuned to specific disparities between the two retinal images, which form the neural substrate for stereoscopic vision. We show that V1 neurons in tree shrews, but not in mice, display highly selective responses to narrow ranges of disparity in random-dot stereograms. Surprisingly, V1 neurons in both species show similarly strong tuning to gratings of varying interocular phase differences. This stimulus-dependent dissociation of disparity tuning can be explained by a network model that combines both feedforward and recurrent connections. The features of the model connections are supported by cortical organizations specific to each species. We validate this model by identifying putative inhibitory neurons and confirming their predicted disparity tuning in both species. Together, our studies establish a foundation for using tree shrews in studying binocular vision and raise an exciting possibility of how cortical columns could be uniquely important in computing stereoscopic depth.

The visual system processes sensory inputs sequentially, perceiving coarse information before fine details. Here we study the neural basis of coarse-to-fine processing and its computational benefits in natural vision. We find that primary visual cortical neurons in awake mice respond to natural scenes in a coarse-to-fine manner, primarily driven by individual neurons rapidly shifting their spatial frequency preference from low to high over a brief response period. This shift transforms the population response in a way that counteracts the statistical regularities of natural scenes, thereby reducing redundancy and generating a more efficient neural representation. The increase in representational efficiency does not occur in either dark-reared or anesthetized mice, which show significantly attenuated coarse-to-fine spatial processing. Collectively, these results illustrate that coarse-to-fine processing is state dependent, develops postnatally via visual experience, and provides a computational advantage by generating more efficient representations of the complex spatial statistics of ethologically relevant natural scenes.

Neurons in the visual system can be spatially organized according to their response properties such as receptive field location and feature selectivity. For example, the visual cortex of many mammalian species contains orientation and direction columns where neurons with similar preferences are clustered. Here, we examine whether such a columnar structure exists in the mouse superior colliculus (SC), a prominent visual center for motion processing. By performing large-scale physiological recording and two-photon calcium imaging in adult male and female mice, we show that direction-selective neurons in the mouse SC are not organized into stereotypical columns as a function of their preferred directions, although clusters of similarly tuned neurons are seen in a minority of mice. Nearby neurons can prefer similar or opposite directions in a largely position-independent manner. This finding holds true regardless of animal state (anesthetized vs awake, running vs stationary), SC depth (most superficial lamina vs deeper in the SC), research technique (calcium imaging vs electrophysiology), and stimulus type (drifting gratings vs moving dots, full field vs small patch). Together, these results challenge recent reports of region-specific organizations in the mouse SC and reveal how motion direction is represented in this important visual center.

Motion streaks are smeared representation of fast-moving objects due to temporal integration. Here, we test for motion streak signals in mice with two-photon calcium imaging. For small dots moving at low speeds, neurons in primary visual cortex (V1) encode the component motion, with preferred direction along the axis perpendicular to their preferred orientation. At high speeds, V1 neurons prefer the direction along the axis parallel to their preferred orientation, as expected for encoding motion streaks. Whereas some V1 neurons (∼20%) display a switch of preferred motion axis with increasing speed, others (>40%) respond specifically to high speeds at the parallel axis. Motion streak neurons are also seen in higher visual lateromedial (LM), anterolateral (AL), and rostrolateral (RL) areas, but with higher transition speeds, and many still prefer the perpendicular axis even with fast motion. Our results thus indicate that diverse motion encoding exists in mouse visual cortex, with intriguing differences among visual areas.