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Primates can recognize objects despite 3D geometric variations such as in-depth rotations. The computational mechanisms that give rise to such invariances are yet to be fully understood. A curious case of partial invariance occurs in the macaque face-patch AL and in fully connected layers of deep convolutional networks in which neurons respond similarly to mirror-symmetric views (e.g. left and right profiles). Why does this tuning develop? Here, we propose a simple learning-driven explanation for mirror-symmetric viewpoint tuning. We show that mirror-symmetric viewpoint tuning for faces emerges in the fully connected layers of convolutional deep neural networks trained on object recognition tasks, even when the training dataset does not include faces. First, using 3D objects rendered from multiple views as test stimuli, we demonstrate that mirror-symmetric viewpoint tuning in convolutional neural network models is not unique to faces: it emerges for multiple object categories with bilateral symmetry. Second, we show why this invariance emerges in the models. Learning to discriminate among bilaterally symmetric object categories induces reflection-equivariant intermediate representations. AL-like mirror-symmetric tuning is achieved when such equivariant responses are spatially pooled by downstream units with sufficiently large receptive fields. These results explain how mirror-symmetric viewpoint tuning can emerge in neural networks, providing a theory of how they might emerge in the primate brain. Our theory predicts that mirror-symmetric viewpoint tuning can emerge as a consequence of exposure to bilaterally symmetric objects beyond the category of faces, and that it can generalize beyond previously experienced object categories.

•Build a large macaque monkey face dataset containing 16,604 real monkey face images.•Develop a DNN to simultaneously classify monkey facial expression and identity.•Perform monkey fMRI experiment to estimate neural responses to monkey face stimuli.•Compare neural responses between DNN layers and monkey face-selective ROIs.•Found representational correspondence between DNN and monkey face processing system. Both the primate visual system and artificial deep neural network (DNN) models show an extraordinary ability to simultaneously classify facial expression and identity. However, the neural computations underlying the two systems are unclear. Here, we developed a multi-task DNN model that optimally classified both monkey facial expressions and identities. By comparing the fMRI neural representations of the macaque visual cortex with the best-performing DNN model, we found that both systems: (1) share initial stages for processing low-level face features which segregate into separate branches at later stages for processing facial expression and identity respectively, and (2) gain more specificity for the processing of either facial expression or identity as one progresses along each branch towards higher stages. Correspondence analysis between the DNN and monkey visual areas revealed that the amygdala and anterior fundus face patch (AF) matched well with later layers of the DNN's facial expression branch, while the anterior medial face patch (AM) matched well with later layers of the DNN's facial identity branch. Our results highlight the anatomical and functional similarities between macaque visual system and DNN model, suggesting a common mechanism between the two systems.

Thirty years ago virtually everything known about primate color vision derived from psychophysical studies of normal and color-defective humans and from physiological investigations of the visual system of the macaque monkey, the most popular of human surrogates for this purpose. The years since have witnessed much progress toward the goal of understanding this remarkable feature of primate vision. Among many advances, investigations focused on naturally occurring variations in color vision in a wide range of nonhuman primate species have proven to be particularly valuable. Results from such studies have been central to our expanding understanding of the interrelationships between opsin genes, cone photopigments, neural organization, and color vision. This work is also yielding valuable insights into the evolution of color vision.

Horizontal cell morphology was studied in the retina of the nocturnal owl-monkey, Aotus, and compared with that of its diurnal, close relative, the capuchin monkey, Cebus. Cells were initially labeled with DiI and the staining was later photoconverted in a stable precipitated using DAB as chromogen. The sizes of cell bodies, dendritic fields, and axon terminals, number of dendritic clusters, intercluster spacing, and intercone spacing were measured at increasing eccentricities. Two distinct morphological classes of horizontal cells were identified, which resembled those of H1 and H3 cells described in diurnal monkeys. A few examples of a third class, possibly corresponding to the H2 cells of diurnal monkeys, were labeled. Both H1 and H3 cells increased in size and had increasing numbers of dendritic clusters with eccentricity. H3 cells were larger and had a larger number of dendritic clusters than H1 cells. Owl-monkey H1 cells had larger dendritic fields than capuchin monkey H1 cells at all quadrants in the central and midperipheral retinal regions, but the difference disappeared in the far periphery. Owl-monkey and capuchin monkey H1 cells had about the same number of dendritic clusters across eccentricity. As owl-monkey H1 cells were larger than capuchin monkey H1 cells, the equal number of clusters in these two primates was due to the fact that they were more spaced in the owl-monkey cells. H1 intercluster distance closely matched intercone spacing for both the owl-monkey and capuchin monkey retinas. On the other hand, H3 intercluster distance was larger than intercone spacing in the retina of both primates. Owl-monkey H1 axon terminals had 2–3 times more knobs than capuchin monkey H1 axon terminals in spite of having about the same size and, consequently, knob density was 2–3 times higher for owl-monkey than capuchin monkey H1 axon terminals across all eccentricities. The differences observed between owl-monkey and capuchin monkey horizontal cells, regarding the morphology of their dendritic trees and axon terminals, may be related to the differences found in the cone-to-rod ratio in the retina of these two primates. They seem to represent retinal specializations to the nocturnal and diurnal life styles of the owl-monkey and capuchin monkey, respectively.

According to the most common point of view, colour vision is a way to conduct an approximate spectral analysis of light. This chapter discusses the role of the invariant relationships between the various perceptual attributes on the one hand, and the attributes of the colour signal code pattern on the other. It argues argue that not only is this analogy appropriate, but the linguistic metaphor in general is quite fruitful for understanding the very essence of visual perception. ‘Primate colour vision has been shaped by the need to find coloured fruits amongst foliage, and the fruits themselves have evolved to be salient to primates and so secure dissemination of their seeds.’ Everyone would, probably, agree that perception is an aspect of animal behaviour. Sensory fabric and perceptual content play the role of the signifier and signified, respectively. However, the linguistic analogy will remain superficial unless we clarify what is meant by visual language.

Jeanne Addison Roberts sees the function of animal imagery in early modern English culture as means to explore what can’t be faced directly: “As agents of revelation, animals operate effectively because they operate to expose a duality in humanity, a haunting combination of the recognized and the strange. Because they are both familiar and mysterious, they can often serve as links between the known and the unknown. In literature and in art, animals often function as guides to previously unexplored psychic landscapes—regions hitherto unsuspected, ignored, or avoided.”1

São Paulo, a densely inhabited state in southeast Brazil that contains the fourth most populated city in the world, recently experienced its largest yellow fever virus (YFV) outbreak in decades. YFV does not normally circulate extensively in São Paulo, so most people were unvaccinated when the outbreak began. Surveillance in non-human primates (NHPs) is important for determining the magnitude and geographic extent of an epizootic, thereby helping to evaluate the risk of YFV spillover to humans. Data from infected NHPs can give more accurate insights into YFV spread than when using data from human cases alone. To contextualise human cases, identify epizootic foci and uncover the rate and direction of YFV spread in São Paulo, we generated and analysed virus genomic data and epizootic case data from NHPs in São Paulo. We report the occurrence of three spatiotemporally distinct phases of the outbreak in São Paulo prior to February 2018. We generated 51 new virus genomes from YFV positive cases identified in 23 different municipalities in São Paulo, mostly sampled from NHPs between October 2016 and January 2018. Although we observe substantial heterogeneity in lineage dispersal velocities between phylogenetic branches, continuous phylogeographic analyses of generated YFV genomes suggest that YFV lineages spread in São Paulo at a mean rate of approximately 1km per day during all phases of the outbreak. Viral lineages from the first epizootic phase in northern São Paulo subsequently dispersed towards the south of the state to cause the second and third epizootic phases there. This alters our understanding of how YFV was introduced into the densely populated south of São Paulo state. Our results shed light on the sylvatic transmission of YFV in highly fragmented forested regions in São Paulo state and highlight the importance of continued surveillance of zoonotic pathogens in sentinel species.

Retinal degenerative diseases caused by photoreceptor cell death are major causes of irreversible vision loss. As only primates have a macula, the nonhuman primate (NHP) models have a crucial role not only in revealing biological mechanisms underlying high-acuity vision but also in the development of therapies. Successful translation of basic research findings into clinical trials and, moreover, approval of the first therapies for blinding inherited and age-related retinal dystrophies has been reported in recent years. This article explores the value of the NHP models in understanding human vision and reviews their contribution to the development of innovative therapeutic strategies to save and restore vision.

Key Points Multiple parallel processing strategies, involving over a dozen retinal ganglion cell types, can be found in the retina. Each ganglion cell type tiles the retina to provide a complete representation across the entire visual field of the visual attributes it conveys to the brain. Three retinal ganglion cell types have been particularly well characterized both anatomically and physiologically and project in parallel from the retina, through the lateral geniculate nucleus of the thalamus to the primary visual cortex. The primary visual cortex receives parallel inputs from the thalamus and uses modularity, defined spatially and by cell type-specific connectivity, to recombine these inputs into new parallel outputs. Beyond the primary visual cortex, separate but interacting dorsal and ventral streams perform distinct computations on similar visual information to support distinct behavioural goals. Less is known about the parallel processing strategies that are used in the extrastriate visual cortex. However, there are strong indications that these areas use many of the same strategies that are found in the primary visual cortex. Many of the parallel processing strategies found in the primate visual system are also found in the other sensory processing systems of the mammalian brain. To integrate our visual environment into a unified and coherent perceptual experience, the brain uses multiple processing strategies. Here, Nassi and Callaway review how the primate primary visual cortex integrates parallel inputs and constructs new, parallel outputs to achieve this goal. Incoming sensory information is sent to the brain along modality-specific channels corresponding to the five senses. Each of these channels further parses the incoming signals into parallel streams to provide a compact, efficient input to the brain. Ultimately, these parallel input signals must be elaborated on and integrated in the cortex to provide a unified and coherent percept. Recent studies in the primate visual cortex have greatly contributed to our understanding of how this goal is accomplished. Multiple strategies including retinal tiling, hierarchical and parallel processing and modularity, defined spatially and by cell type-specific connectivity, are used by the visual system to recover the intricate detail of our visual surroundings.