Annual Review of Vision Science - Volume 5, 2019

We are sad to report that Professor Jacob (Jack) Nachmias passed away on March 2, 2019. Nachmias was born in Athens, Greece, on June 9, 1928. To escape the Nazis, he and his family came to the United States in 1939. He received his undergraduate degree from Cornell University and then an MA from Swarthmore College, where he worked with Hans Wallach and Wolfgang Kohler; his PhD in Psychology was from Harvard University. Nachmias spent the majority of his career as a Professor of Psychology at the University of Pennsylvania. He made fundamental contributions to our understanding of vision, most notably through the study of eye movements, the development of signal detection theory and forced-choice psychophysical methods, and the psychophysical characterization of spatial-frequency-selective visual channels. Nachmias' work was recognized by his election to the National Academy of Sciences and receipt of the Optical Society's Tillyer Award.

Retinal function has long been studied with psychophysical methods in humans, whereas detailed functional studies of vision have been conducted mostly in animals owing to the invasive nature of physiological approaches. There are exceptions to this generalization, for example, the electroretinogram. This review examines exciting recent advances using in vivo retinal imaging to understand the function of retinal neurons. In some cases, the methods have existed for years and are still being optimized. In others, new methods such as optophysiology are revealing novel patterns of retinal function in animal models that have the potential to change our understanding of the functional capacity of the retina. Together, the advances in retinal imaging mark an important milestone that shifts attention away from anatomy alone and begins to probe the function of healthy and diseased eyes.

Refractive errors are the product of a mismatch between the axial length of the eye and its optical power, creating blurred vision. Uncorrected refractive errors are the second leading cause of worldwide blindness. One refractive error currently attracting significant scientific interest is myopia, mostly owing to the recent rise in its prevalence worldwide and associated ocular disease burden. This increase in myopia prevalence has also been rapid, suggesting environmental influences in addition to any genetic influences on eye growth. This review defines refractive errors, describes their prevalence, and presents evidence for the influence of genetic and environmental factors related to refractive error development.

Rods and cones are retinal photoreceptor neurons required for our visual sensation. Because of their highly polarized structures and well-characterized processes of G protein–coupled receptor–mediated phototransduction signaling, these photoreceptors have been excellent models for studying the compartmentalization and sorting of proteins. Rods and cones have a modified ciliary compartment called the outer segment (OS) as well as non-OS compartments. The distinct membrane protein compositions between OS and non-OS compartments suggest that the OS is separated from the rest of the cellular compartments by multiple barriers or gates that are selectively permissive to specific cargoes. This review discusses the mechanisms of protein sorting and compartmentalization in photoreceptor neurons. Proper sorting and compartmentalization of membrane proteins are required for signal transduction and transmission. This review also discusses the roles of compartmentalized signaling, which is compromised in various retinal ciliopathies.

Inflammation of the blood vessels that serve the central nervous system has been increasingly identified as an early and possibly initiating event among neurodegenerative conditions such as Alzheimer's disease and related dementias. However, the causal relevance of vascular inflammation to major retinal degenerative diseases is unresolved. Here, we describe how genetics, aging-associated changes, and environmental factors contribute to vascular inflammation in age-related macular degeneration, diabetic retinopathy, and glaucoma. We highlight the importance of mouse models in studying the underlying mechanisms and possible treatments for these diseases. We conclude that data support vascular inflammation playing a central if not primary role in retinal degenerative diseases, and this association should be a focus of future research.

Cataract, the clinical correlate of opacity or light scattering in the eye lens, is usually caused by the presence of high-molecular-weight (HMW) protein aggregates or disruption of the lens microarchitecture. In general, genes involved in inherited cataracts reflect important processes and pathways in the lens including lens crystallins, connexins, growth factors, membrane proteins, intermediate filament proteins, and chaperones. Usually, mutations causing severe damage to proteins cause congenital cataracts, while milder variants increasing susceptibility to environmental insults are associated with age-related cataracts. These may have different pathogenic mechanisms: Congenital cataracts induce the unfolded protein response and apoptosis. By contrast, denatured crystallins in age-related cataracts are bound by α-crystallin and form light-scattering HMW aggregates. New therapeutic approaches to age-related cataracts use chemical chaperones to solubilize HMW aggregates, while attempts are being made to regenerate lenses using endogenous stem cells to treat congenital cataracts.

Fuchs endothelial corneal dystrophy (FECD) is a bilateral corneal endothelial disorder and the most common cause of corneal transplantation worldwide. Professor Ernst Fuchs described the first 13 cases of FECD more than 100 years ago. Since then, we have seen far-reaching progress in its diagnosis and treatment. In the field of diagnostics, new technologies enable the development of more accurate classification systems and the more detailed breakdown of the genetic basis of FECD. Laboratory studies help in deciphering the molecular pathomechanisms. The development of minimally invasive surgical techniques leads to a continuous improvement of the postoperative result. This review highlights and discusses clinical, genetic, pathophysiologic, and therapeutic aspects of this common and important corneal disorder.

The jawless fish that were ancestral to all living vertebrates had four spectral cone types that were probably served by chromatic-opponent retinal circuits. Subsequent evolution of photoreceptor spectral sensitivities is documented for many vertebrate lineages, giving insight into the ecological adaptation of color vision. Beyond the photoreceptors, retinal color processing is best understood in mammals, especially the blue^ON system, which opposes short- against long-wavelength receptor responses. For other vertebrates that often have three or four types of cone pigment, new findings from zebrafish are extending older work on teleost fish and reptiles to reveal rich color circuitry. Here, horizontal cells establish diverse and complex spectral responses even in photoreceptor outputs. Cone-selective connections to bipolar cells then set up color-opponent synaptic layers in the inner retina, which lead to a large variety of color-opponent channels for transmission to the brain via retinal ganglion cells.

Numerous studies have demonstrated the impact of imposed abnormal visual experience on the postnatal development of the visual system. These studies have provided fundamental insights into the mechanisms underlying neuroplasticity and its role in clinical care. However, the ocular motor responses of postnatal human infants largely define their visual experience in dynamic three-dimensional environments. Thus, the immature visual system needs to control its own visual experience. This review explores the interaction between the developing motor and sensory/perceptual visual systems, together with its importance in both typical development and the development of forms of strabismus and amblyopia.

Smooth pursuit eye movements maintain the line of sight on smoothly moving targets. Although often studied as a response to sensory motion, pursuit anticipates changes in motion trajectories, thus reducing harmful consequences due to sensorimotor processing delays. Evidence for predictive pursuit includes (a) anticipatory smooth eye movements (ASEM) in the direction of expected future target motion that can be evoked by perceptual cues or by memory for recent motion, (b) pursuit during periods of target occlusion, and (c) improved accuracy of pursuit with self-generated or biologically realistic target motions. Predictive pursuit has been linked to neural activity in the frontal cortex and in sensory motion areas. As behavioral and neural evidence for predictive pursuit grows and statistically based models augment or replace linear systems approaches, pursuit is being regarded less as a reaction to immediate sensory motion and more as a predictive response, with retinal motion serving as one of a number of contributing cues.

The cerebellar cortex is a crystal-like structure consisting of an almost endless repetition of a canonical microcircuit that applies the same computational principle to different inputs. The output of this transformation is broadcasted to extracerebellar structures by way of the deep cerebellar nuclei. Visually guided eye movements are accommodated by different parts of the cerebellum. This review primarily discusses the role of the oculomotor part of the vermal cerebellum [the oculomotor vermis (OMV)] in the control of visually guided saccades and smooth-pursuit eye movements. Both types of eye movements require the mapping of retinal information onto motor vectors, a transformation that is optimized by the OMV, considering information on past performance. Unlike the role of the OMV in the guidance of eye movements, the contribution of the adjoining vermal cortex to visual motion perception is nonmotor and involves a cerebellar influence on information processing in the cerebral cortex.

Visual stimuli can evoke complex behavioral responses, but the underlying streams of neural activity in mammalian brains are difficult to follow because of their size. Here, I review the visual system of zebrafish larvae, highlighting where recent experimental evidence has localized the functional steps of visuomotor transformations to specific brain areas. The retina of a larva encodes behaviorally relevant visual information in neural activity distributed across feature-selective ganglion cells such that signals representing distinct stimulus properties arrive in different areas or layers of the brain. Motor centers in the hindbrain encode motor variables that are precisely tuned to behavioral needs within a given stimulus setting. Owing to rapid technological progress, larval zebrafish provide unique opportunities for obtaining a comprehensive understanding of the intermediate processing steps occurring between visual and motor centers, revealing how visuomotor transformations are implemented in a vertebrate brain.

Recent data have shown that sleep plays a beneficial role for cognitive functions such as declarative memory consolidation and perceptual learning. In this article, we review recent findings on the role of sleep in promoting adaptive visual response changes in the lateral geniculate nucleus and primary visual cortex following novel visual experiences. We discuss these findings in the context of what is currently known about how sleep affects the activity and function of thalamocortical circuits and current hypotheses regarding how sleep facilitates synaptic plasticity.

In this article, we review the anatomical inputs and outputs to the mouse primary visual cortex, area V1. Our survey of data from the Allen Institute Mouse Connectivity project indicates that mouse V1 is highly interconnected with both cortical and subcortical brain areas. This pattern of innervation allows for computations that depend on the state of the animal and on behavioral goals, which contrasts with simple feedforward, hierarchical models of visual processing. Thus, to have an accurate description of the function of V1 during mouse behavior, its involvement with the rest of the brain circuitry has to be considered. Finally, it remains an open question whether the primary visual cortex of higher mammals displays the same degree of sensorimotor integration in the early visual system.

Our assignment was to review the development of the face-processing network, an assignment that carries the presupposition that a face-specific developmental program exists. We hope to cast some doubt on this assumption and instead argue that the development of face processing is guided by the same ubiquitous rules that guide the development of cortex in general.

Humans are remarkably adept at perceiving and understanding complex real-world scenes. Uncovering the neural basis of this ability is an important goal of vision science. Neuroimaging studies have identified three cortical regions that respond selectively to scenes: parahippocampal place area, retrosplenial complex/medial place area, and occipital place area. Here, we review what is known about the visual and functional properties of these brain areas. Scene-selective regions exhibit retinotopic properties and sensitivity to low-level visual features that are characteristic of scenes. They also mediate higher-level representations of layout, objects, and surface properties that allow individual scenes to be recognized and their spatial structure ascertained. Challenges for the future include developing computational models of information processing in scene regions, investigating how these regions support scene perception under ecologically realistic conditions, and understanding how they operate in the context of larger brain networks.

Artificial vision has often been described as one of the key remaining challenges to be solved before machines can act intelligently. Recent developments in a branch of machine learning known as deep learning have catalyzed impressive gains in machine vision—giving a sense that the problem of vision is getting closer to being solved. The goal of this review is to provide a comprehensive overview of recent deep learning developments and to critically assess actual progress toward achieving human-level visual intelligence. I discuss the implications of the successes and limitations of modern machine vision algorithms for biological vision and the prospect for neuroscience to inform the design of future artificial vision systems.

Adaptation is a common principle that recurs throughout the nervous system at all stages of processing. This principle manifests in a variety of phenomena, from spike frequency adaptation, to apparent changes in receptive fields with changes in stimulus statistics, to enhanced responses to unexpected stimuli. The ubiquity of adaptation leads naturally to the question: What purpose do these different types of adaptation serve? A diverse set of theories, often highly overlapping, has been proposed to explain the functional role of adaptive phenomena. In this review, we discuss several of these theoretical frameworks, highlighting relationships among them and clarifying distinctions. We summarize observations of the varied manifestations of adaptation, particularly as they relate to these theoretical frameworks, focusing throughout on the visual system and making connections to other sensory systems.

With modern neurophysiological methods able to record neural activity throughout the visual pathway in the context of arbitrarily complex visual stimulation, our understanding of visual system function is becoming limited by the available models of visual neurons that can be directly related to such data. Different forms of statistical models are now being used to probe the cellular and circuit mechanisms shaping neural activity, understand how neural selectivity to complex visual features is computed, and derive the ways in which neurons contribute to systems-level visual processing. However, models that are able to more accurately reproduce observed neural activity often defy simple interpretations. As a result, rather than being used solely to connect with existing theories of visual processing, statistical modeling will increasingly drive the evolution of more sophisticated theories.

Human vision provides useful information about the shape and color of the objects around us. It works well in many, but not all, lighting conditions. Since the advent of human-made light sources, it has been important to understand how illumination affects vision quality, but this has been surprisingly difficult. The widespread introduction of solid-state light emitters has increased the urgency of this problem. Experts still debate how lighting can best enable high-quality vision—a key issue since about one-fifth of global electrical power production is used to make light. Photometry, the measurement of the visual quantity of light, is well established, yet significant uncertainties remain. Colorimetry, the measurement of color, has achieved good reproducibility, but researchers still struggle to understand how illumination can best enable high-quality color vision. Fortunately, in recent years, considerable progress has been made. Here, we summarize the current understanding and discuss key areas for future study.

To understand the processes behind seeing light, we need to integrate knowledge about the incoming optical structure, its perception, and how light interacts with material, shape, and space—objectively and subjectively. To that end, we need a novel approach to the science of light, namely, a transdisciplinary science of appearance, integrating optical, perceptual, and design knowledge and methods. In this article, I review existing literature as a basis for such a synthesis, which should discuss light in its full complexity, including its spatial properties and interactions with materials, shape, and space. I propose to investigate this by representing the endless variety of light, materials, shapes, and space as canonical modes and their combinations.

Virtual reality (VR) is becoming an increasingly important way to investigate sensory processing. The converse is also true: in order to build good VR technologies, one needs an intimate understanding of how our brain processes sensory information. One of the key advantages of studying perception with VR is that it allows an experimenter to probe perceptual processing in a more naturalistic way than has been possible previously. In VR, one is able to actively explore and interact with the environment, just as one would do in real life. In this article, we review the history of VR displays, including the philosophical origins of VR, before discussing some key challenges involved in generating good VR and how a sense of presence in a virtual environment can be measured. We discuss the importance of multisensory VR and evaluate the experimental tension that exists between artifice and realism when investigating sensory processing.

From mainstream media outlets to social media and everything in between, doctored photographs are appearing with growing frequency and sophistication. The resulting lack of trust is impacting law enforcement, national security, the media, e-commerce, and more. While some types of manipulations can be detected with a careful visual examination, our visual system seems unable to reliably detect other types of manipulations. The field of image forensics has emerged to help return some trust in photography. I describe the perceptual limits of detecting manipulated images, as well as representative examples of computational techniques for authenticating images.