Antagonistic interactions between center and surround regions of the receptive field are widely observed across sensory systems. In the early visual system, these interactions contribute to important computations such as edge detection. However, less is known about how center-surround interactions depend on the spatiotemporal properties of the visual input. Here, we show that surround motion strongly modulates the response properties of two understudied primate ganglion cell types. Broad thorny cell responses are strongest when motion in the center and surround is uncorrelated, similar to object-motion-sensitive cells found in other species. A different pattern is observed in On smooth monostratified cells: surround activation is suppressive for static stimuli and facilitatory for motion. These effects of surround activation diverge significantly from classical center-surround models and more closely resemble how surround motion affects responses in primate visual cortex.

The primate retina has traditionally been viewed as a simple, high-resolution encoding stage, performing basic spatial, temporal, and chromatic filtering before transmitting information to cortical areas for higher-level processing. This perspective, formalized in the classical three-channel model, emphasizes three dominant ganglion cell pathways: parasol cells for achromatic signals, midget cells for red-green opponency and spatial detail, and small bistratified cells for blue-yellow signals. However, recent evidence challenges this view, showing that these pathways play more complex and nuanced roles than previously assumed. In non-primate species, the retina is now recognized as a site of substantial computation, performing nonlinear processing, motion analysis, and feature extraction. Emerging data suggest that similar computational complexity exists in the primate retina but has been underappreciated due to sampling biases and the use of overly simplistic stimuli. Anatomical and physiological studies indicate that the primate retina contains at least 20-30 distinct ganglion cell types, many exhibiting feature selectivity analogous to that seen in rodents, rabbits, and amphibians. In this review, we critically examine the classical model of primate retinal processing, its historical foundations, and its limitations. We highlight recent evidence revealing a more complex and diverse primate retinal output and discuss how conserved and primate-specific circuits contribute to early visual computations. Together, these findings reposition the primate retina as an active computational structure, not merely a simple front-end encoder.

Understanding the structure-function relationships across neurons is challenging, particularly when circuits are composed of dozens of distinct cell types. We refined an approach, called “projection targeting with phototagging”, that allows simultaneous elucidation of the projections, morphology, and visual response properties of diverse retinal ganglion cell (RGC) types in the mammalian retina. The approach combines retrograde virally mediated phototagging of RGCs, microscopy, and large-scale multi-electrode array (MEA) measurements. Importantly, the approach does not rely on transgenic animals and thus is potentially generalizable across species. We validated this approach in rats by targeting retinal projections to the superior colliculus (SC). We showed that multiple RGC types project to the SC and that these results in rats align well with prior findings from transgenic mouse studies.

Strategies to stimulate the regeneration of neurons in the adult central nervous system can offer universal solutions for neurodegenerative diseases. Taking lessons from naturally regenerating species, such as the zebrafish, we have previously shown that vector-mediated expression of proneural transcription factors can stimulate neurogenesis from the resident Müller glia (MG) population in the adult mouse retina, both in vitro and in vivo . To bring this closer to translation, we now show that vector-mediated expression of the proneural transcription factor ASCL1 can reprogram adult macaque MG into functional neurons. To this end, we established purified MG cultures and show they retain a mature transcriptomic profile that correlates with foveal and peripheral MG. Importantly, MG-derived neurons express retinal ganglion cell markers, can fire action potentials and have a transcriptome that overlaps with developing human and adult macaque retinal ganglion cells. To refine this approach for clinical application, we incorporated microRNA-124 target sites in the reprogramming cassette and show that this restricts expression to MG in mixed primary cultures and intact explant cultures of adult macaque retina. Regulating ASCL1 expression with microRNA-124 target sites maintained the reprogramming efficiency from adult MG cultures and improved the yield of RGC-like neurons from infant MG cultures. Most importantly, with this vector cassette we successfully reprogrammed macaque MG from both adult and infant retina into HuC/D+ neurons. Our findings demonstrate that ASCL1 can induce neurogenesis from macaque MG across ages and provide a targeted, effective strategy for potential clinical translation in retinal repair.

Adaptation is a universal aspect of neural systems that changes circuit computations to match prevailing inputs. These changes facilitate efficient encoding of sensory inputs while avoiding saturation. Conventional artificial neural networks (ANNs) have limited adaptive capabilities, hindering their ability to reliably predict neural output under dynamic input conditions. Can embedding neural adaptive mechanisms in ANNs improve their performance? To answer this question, we develop a new deep learning model of the retina that incorporates the biophysics of photoreceptor adaptation at the front-end of conventional convolutional neural networks (CNNs). These conventional CNNs build on 'Deep Retina,' a previously developed model of retinal ganglion cell (RGC) activity. CNNs that include this new photoreceptor layer outperform conventional CNN models at predicting male and female primate and rat RGC responses to naturalistic stimuli that include dynamic local intensity changes and large changes in the ambient illumination. These improved predictions result directly from adaptation within the phototransduction cascade. This research underscores the potential of embedding models of neural adaptation in ANNs and using them to determine how neural circuits manage the complexities of encoding natural inputs that are dynamic and span a large range of light levels.

Purpose: Investigate associations of natural environmental exposures with exudative and nonexudative age -related macular degeneration (AMD) across the United States.Design: Database study.Participants: Patients aged > 55 years who were active in the IRIS Registry from 2016 to 2018 were analyzed. Patients were categorized as nonexudative, inactive exudative, and active exudative AMD by Inter-national Classification of Diseases 10th Revision and Current Procedural Terminology (CPT) codes. Patients without provider-level ZIP codes matching any ZIP code tabulation area were excluded.Methods: Environmental data were obtained from public sources including the US Geological Survey, Na-tional Renewable Energy Laboratory, National Oceanic and Atmospheric Administration, and Environmental Protection Agency. Multiple variable, mixed effects logistic regression models with random intercepts per ZIP code tabulation area quantified the association of each environmental variable with any AMD versus non-AMD patients, any exudative AMD versus nonexudative AMD, and active exudative AMD versus inactive exudative and nonexudative AMD using 3 separate models, while adjusting for age, sex, race, insurance type, smoking history, and phakic status.Main Outcome Measure: Odds ratios for environmental factors.Results: A total of 9 884 527 patients were included. Elevation, latitude, solar irradiance measured in global horizontal irradiance (GHI) and direct normal irradiance (DNI), temperature and precipitation variables, and pollution variables were included in our models. Statistically significant associations with active exudative AMD were GHI (odds ratio [OR], 3.848; 95% confidence interval [CI] with Bonferroni correction, 1.316-11.250), DNI (OR, 0.581; 95% CI, 0.370-0.913), latitude (OR, 1.110; 95% CI, 1.046-1.178), ozone (OR, 1.014; 95% CI, 1.004-1.025), and nitrogen dioxide (OR, 1.005; 95% CI, 1.000-1.010). The only significant environmental as-sociations with any AMD were inches of snow in the winter (OR, 1.005; 95% CI, 1.001-1.009) and ozone (OR, 1.011; 95% CI, 1.003-1.019).Conclusions: The strongest environmental associations differed between AMD subgroups. The solar vari-ables GHI, DNI, and latitude were significantly associated with active exudative AMD. Two pollutant variables, ozone and nitrogen dioxide, also showed positive associations with AMD. Further studies are warranted to investigate the clinical relevance of these associations. Our curated environmental dataset has been made publicly available at https://github.com/uw-biomedical-ml/AMD_environmental_dataset. Ophthalmology Science 2022;2:100195 & COPY; 2022 by the American Academy of Ophthalmology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

The visual image transmitted by the retina to the brain has long been understood in terms of spatial filtering by the center-surround receptive fields of retinal ganglion cells (RGCs). Recently, this textbook view has been challenged by the stunning functional diversity and specificity observed in ∼40 distinct RGC types in the mouse retina. However, it is unclear whether the ∼20 morphologically and molecularly identified RGC types in primates exhibit similar functional diversity, or instead exhibit center-surround organization at different spatial scales. Here, we reveal striking and surprising functional diversity in macaque and human RGC types using large-scale multi-electrode recordings from isolated macaque and human retinas. In addition to the five well-known primate RGC types, 18-27 types were distinguished by their functional properties, likely revealing several previously unknown types. Surprisingly, many of these cell types exhibited striking non-classical receptive field structure, including irregular spatial and chromatic properties not previously reported in any species. Qualitatively similar results were observed in recordings from the human retina. The receptive fields of less-understood RGC types formed uniform mosaics covering visual space, confirming their classification, and the morphological counterparts of two types were established using single-cell recording. The striking receptive field diversity was paralleled by distinctive responses to natural movies and complexity of visual computation. These findings suggest that diverse RGC types, rather than merely filtering the scene at different spatial scales, instead play specialized roles in human vision.

Some visual properties are consistent across a wide range of environments, while other properties are more labile. The efficient coding hypothesis states that many of these regularities in the environment can be discarded from neural representations, thus allocating more of the brain's dynamic range to properties that are likely to vary. This paradigm is less clear about how the visual system prioritizes different pieces of information that vary across visual environments. One solution is to prioritize information that can be used to predict future events, particularly those that guide behavior. The relationship between the efficient coding and future prediction paradigms is an area of active investigation. In this review, we argue that these paradigms are complementary and often act on distinct components of the visual input. We also discuss how normative approaches to efficient coding and future prediction can be integrated.

We can distinguish between the direction and speed of a moving object effortlessly, but this is actually a very challenging computational task. A new study demonstrates that this process begins at the first stages of visual processing in the retina.

ABSTRACTSuccessful behavior relies on the ability to use information obtained from past experience to predict what is likely to occur in the future. A salient example of predictive encoding comes from the vertebrate retina, where neural circuits encode information that can be used to estimate the trajectory of a moving object. Predictive computations should be a general property of sensory systems, but the features needed to identify these computations across neural systems are not well understood. Here, we identify several properties of predictive computations in the primate retina that likely generalize across sensory systems. These features include calculating the derivative of incoming signals, sparse signal integration, and delayed response suppression. These findings provide a deeper understanding of how the brain carries out predictive computations and identify features that can be used to recognize these computations throughout the brain.

To efficiently navigate through the environment and avoid potential threats, an animal must quickly detect the motion of approaching objects. Current models of primate vision place the origins of this complex computation in the visual cortex. Here, we report that detection of approaching motion begins in the retina. Several ganglion cell types, the retinal output neurons, show selectivity to approaching motion. Synaptic current recordings from these cells further reveal that this preference for approaching motion arises in the interplay between presynaptic excitatory and inhibitory circuit elements. These findings demonstrate how excitatory and inhibitory circuits interact to mediate an ethologically relevant neural function. Moreover, the elementary computations that detect approaching motion begin early in the visual stream of primates.