Retinal origin of orientation maps in visual cortex

Study of tree shrew biology and models: A booming and prosperous field for biomedical research

The tree shrew ( Tupaia belangeri) has long been proposed as a suitable alternative to non-human primates (NHPs) in biomedical and laboratory research due to its close evolutionary relationship with primates. In recent years, significant advances have facilitated tree shrew studies, including the determination of the tree shrew genome, genetic manipulation using spermatogonial stem cells, viral vector-mediated gene delivery, and mapping of the tree shrew brain atlas. However, the limited availability of tree shrews globally remains a substantial challenge in the field. Additionally, determining the key questions best answered using tree shrews constitutes another difficulty. Tree shrew models have historically been used to study hepatitis B virus (HBV) and hepatitis C virus (HCV) infection, myopia, and psychosocial stress-induced depression, with more recent studies focusing on developing animal models for infectious and neurodegenerative diseases. Despite these efforts, the impact of tree shrew models has not yet matched that of rodent or NHP models in biomedical research. This review summarizes the prominent advancements in tree shrew research and reflects on the key biological questions addressed using this model. We emphasize that intensive dedication and robust international collaboration are essential for achieving breakthroughs in tree shrew studies. The use of tree shrews as a unique resource is expected to gain considerable attention with the application of advanced techniques and the development of viable animal models, meeting the increasing demands of life science and biomedical research.

Orientation selectivity mapping in the visual cortex

The orientation map is one of the most well-studied functional maps of the visual cortex. However, results from the literature are of different qualities. Clear boundaries among different orientation domains and blurred uncertain distinctions were shown in different studies. These unclear imaging results will lead to an inaccuracy in depicting cortical structures, and the lack of consideration in experimental design will also lead to biased depictions of the cortical features. How we accurately define orientation domains will impact the entire field of research. In this study, we test how spatial frequency (SF), stimulus size, location, chromatic, and data processing methods affect the orientation functional maps (including a large area of dorsal V4, and parts of dorsal V1) acquired by intrinsic signal optical imaging. Our results indicate that, for large imaging fields, large grating stimuli with mixed SF components should be considered to acquire the orientation map. A diffusion model image enhancement based on the difference map could further improve the map quality. In addition, the similar outcomes of achromatic and chromatic gratings indicate two alternative types of afferents from LGN, pooling in V1 to generate cue-invariant orientation selectivity.

Optimization-Based Pairwise Interaction Point Process (O-PIPP): A Precise and Universal Retinal Mosaic Modeling Approach

Purpose

A retinal mosaic, the spatial organization of a population of homotypic neurons, is thought to sample a specific visual feature into the feedforward visual pathway. The purpose of this study was to propose a universal modeling approach for precisely generating retinal mosaics and overcoming the limitations of previous models, especially in modeling abnormal mosaic patterns under disease conditions.

Methods

Here, we developed the optimization-based pairwise interaction point process (O-PIPP). It incorporates optimization techniques into previous simulation approaches, enabling directional control of the simulation process according to the user-designed optimization target. For the convenience of the community, we implemented the O-PIPP approach into a Python package and a website application.

Results

We showed that the O-PIPP can generate more precise neural spatial patterns of healthy and diseased mosaics compared to previous phenomenological approaches. Notably, through modeling the retinal neural circuitry with O-PIPP-simulated retinitis pigmentosa cone mosaics, we elucidated how the cone mosaic rearrangement impacted the information processing of ganglion cells.

Conclusions

The O-PIPP provides a precise and universal tool to simulate realistic mosaics, which could help to investigate the function of retinal mosaics in vision.

Robust variability of grid cell properties within individual grid modules enhances encoding of local space

Although grid cells are one of the most well studied functional classes of neurons in the mammalian brain, the assumption that there is a single grid orientation and spacing per grid module has not been carefully tested. We investigate and analyze a recent large-scale recording of medial entorhinal cortex to characterize the presence and degree of heterogeneity of grid properties within individual modules. We find evidence for small, but robust, variability and hypothesize that this property of the grid code could enhance the ability of encoding local spatial information. Performing analysis on synthetic populations of grid cells, where we have complete control over the amount heterogeneity in grid properties, we demonstrate that variability, of a similar magnitude to the analyzed data, leads to significantly decreased decoding error, even when restricted to activity from a single module. Our results highlight how the heterogeneity of the neural response properties may benefit coding and opens new directions for theoretical and experimental analysis of grid cells.

Self-organization of modular activity in immature cortical networks

During development, cortical activity is organized into distributed modular patterns that are a precursor of the mature columnar functional architecture. Theoretically, such structured neural activity can emerge dynamically from local synaptic interactions through a recurrent network with effective local excitation with lateral inhibition (LE/LI) connectivity. Utilizing simultaneous widefield calcium imaging and optogenetics in juvenile ferret cortex prior to eye opening, we directly test several critical predictions of an LE/LI mechanism. We show that cortical networks transform uniform stimulations into diverse modular patterns exhibiting a characteristic spatial wavelength. Moreover, patterned optogenetic stimulation matching this wavelength selectively biases evoked activity patterns, while stimulation with varying wavelengths transforms activity towards this characteristic wavelength, revealing a dynamic compromise between input drive and the network's intrinsic tendency to organize activity. Furthermore, the structure of early spontaneous cortical activity - which is reflected in the developing representations of visual orientation - strongly overlaps that of uniform opto-evoked activity, suggesting a common underlying mechanism as a basis for the formation of orderly columnar maps underlying sensory representations in the brain.

Common modular architecture across diverse cortical areas in early development

In order to deal with a complex environment, animals form a diverse range of neural representations that vary across cortical areas, ranging from largely unimodal sensory input to higher-order representations of goals, outcomes, and motivation. The developmental origin of this diversity is currently unclear, as representations could arise through processes that are already area-specific from the earliest developmental stages or alternatively, they could emerge from an initially common functional organization shared across areas. Here, we use spontaneous activity recorded with two-photon and widefield calcium imaging to reveal the functional organization across the early developing cortex in ferrets, a species with a well-characterized columnar organization and modular structure of spontaneous activity in the visual cortex. We find that in animals 7 to 14 d prior to eye-opening and ear canal opening, spontaneous activity in both sensory areas (auditory and somatosensory cortex, A1 and S1, respectively), and association areas (posterior parietal and prefrontal cortex, PPC and PFC, respectively) showed an organized and modular structure that is highly similar to the organization in V1. In all cortical areas, this modular activity was distributed across the cortical surface, forming functional networks that exhibit millimeter-scale correlations. Moreover, this modular structure was evident in highly coherent spontaneous activity at the cellular level, with strong correlations among local populations of neurons apparent in all cortical areas examined. Together, our results demonstrate a common distributed and modular organization across the cortex during early development, suggesting that diverse cortical representations develop initially according to similar design principles.

Self-organization of modular activity in immature cortical networks

During development, cortical activity is organized into distributed modular patterns that are a precursor of the mature columnar functional architecture. Theoretically, such structured neural activity can emerge dynamically from local synaptic interactions through a recurrent network with effective local excitation with lateral inhibition (LE/LI) connectivity. Utilizing simultaneous widefield calcium imaging and optogenetics in juvenile ferret cortex prior to eye opening, we directly test several critical predictions of an LE/LI mechanism. We show that cortical networks transform uniform stimulations into diverse modular patterns exhibiting a characteristic spatial wavelength. Moreover, patterned optogenetic stimulation matching this wavelength selectively biases evoked activity patterns, while stimulation with varying wavelengths transforms activity towards this characteristic wavelength, revealing a dynamic compromise between input drive and the network's intrinsic tendency to organize activity. Furthermore, the structure of early spontaneous cortical activity - which is reflected in the developing representations of visual orientation - strongly overlaps that of uniform opto-evoked activity, suggesting a common underlying mechanism as a basis for the formation of orderly columnar maps underlying sensory representations in the brain.

Impact of light-emitting diodes on visual cortex layer 5 pyramidal neurons (V1-L5PNs)-A rodent study

Purpose

Light-induced neural retinal insult leads to alterations in the visual cortex neurons. We examined light-induced neuronal alterations in the visual cortex layer 5 pyramidal neurons (V1-L5PNs) of adult male Wistar rats.

Methods

A total of 24 rats were divided into the following groups (n=6 each): control (NC), blue (BL), white (WL), and yellow (YL). The exposure groups were subjected to light-emitting diodes (LED) exposure (450-500 lx) of differing wavelengths for 90 days (12:12 16 light-dark cycle). After LED exposure, the animals were sacrificed, and the brain tissues were removed and impregnated in freshly prepared Golgi-Cox stain for 21 days. Sholl's grading analysis was used to quantify the apical and basal dendritic branching points and intersections of the V1-L5PNs.

Results

There was a significant difference in the number of apical branching points among all groups (p<0.001), with a particularly notable difference between the BL and WL groups (p<0.001). A post hoc test revealed that all exposure groups (BL, WL, and YL) had fewer apical branching points (p<0.001) on an average of 3.6 µm and a significant reduction in the dendritic intersections (p<0.001) compared to the number of branching points extending from layer Va (V1) neurons.

Conclusions

Chronic and cumulative exposure to blue and white LEDs led to the pruning of V1-L5PNs, which might impair visual processing.

Comparison of visual quantities in untrained neural networks

The ability to compare quantities of visual objects with two distinct measures, proportion and difference, is observed even in newborn animals. However, how this function originates in the brain, even before visual experience, remains unknown. Here, we propose a model in which neuronal tuning for quantity comparisons can arise spontaneously in completely untrained neural circuits. Using a biologically inspired model neural network, we find that single units selective to proportions and differences between visual quantities emerge in randomly initialized feedforward wirings and that they enable the network to perform quantity comparison tasks. Notably, we find that two distinct tunings to proportion and difference originate from a random summation of monotonic, nonlinear neural activities and that a slight difference in the nonlinear response function determines the type of measure. Our results suggest that visual quantity comparisons are primitive types of functions that can emerge spontaneously before learning in young brains.

Species-specific wiring of cortical circuits for small-world networks in the primary visual cortex

Long-range horizontal connections (LRCs) are conspicuous anatomical structures in the primary visual cortex (V1) of mammals, yet their detailed functions in relation to visual processing are not fully understood. Here, we show that LRCs are key components to organize a "small-world network" optimized for each size of the visual cortex, enabling the cost-efficient integration of visual information. Using computational simulations of a biologically inspired model neural network, we found that sparse LRCs added to networks, combined with dense local connections, compose a small-world network and significantly enhance image classification performance. We confirmed that the performance of the network appeared to be strongly correlated with the small-world coefficient of the model network under various conditions. Our theoretical model demonstrates that the amount of LRCs to build a small-world network depends on each size of cortex and that LRCs are beneficial only when the size of the network exceeds a certain threshold. Our model simulation of various sizes of cortices validates this prediction and provides an explanation of the species-specific existence of LRCs in animal data. Our results provide insight into a biological strategy of the brain to balance functional performance and resource cost.

Developmental alignment of feedforward inputs and recurrent network activity drives increased response selectivity and reliability in primary visual cortex following the onset of visual experience

Selective and reliable cortical sensory representations depend on synaptic interactions between feedforward inputs, conveying information from lower levels of the sensory pathway, and recurrent networks that reciprocally connect neurons functioning at the same hierarchical level. Here we explore the development of feedforward/recurrent interactions in primary visual cortex of the ferret that is responsible for the representation of orientation, focusing on the feedforward inputs from cortical layer 4 and its relation to the modular recurrent network in layer 2/3 before and after the onset of visual experience. Using simultaneous laminar electrophysiology and calcium imaging we found that in experienced animals, individual layer 4 and layer 2/3 neurons exhibit strongly correlated responses with the modular recurrent network structure in layer 2/3. Prior to experience, layer 2/3 neurons exhibit comparable modular correlation structure, but this correlation structure is missing for individual layer 4 neurons. Further analysis of the receptive field properties of layer 4 neurons in naïve animals revealed that they exhibit very poor orientation tuning compared to layer 2/3 neurons at this age, and this is accompanied by the lack of spatial segregation of ON and OFF subfields, the definitive property of layer 4 simple cells in experienced animals. Analysis of the response dynamics of layer 2/3 neurons with whole-cell patch recordings confirms that individual layer 2/3 neurons in naïve animals receive poorly-selective feedforward input that does not align with the orientation preference of the layer 2/3 responses. Further analysis reveals that the misaligned feedforward input is the underlying cause of reduced selectivity and increased response variability that is evident in the layer 2/3 responses of naïve animals. Altogether, our experiments indicate that the onset of visual experience is accompanied by a critical refinement in the responses of layer 4 neurons and the alignment of feedforward and recurrent networks that increases the selectivity and reliability of the representation of orientation in V1.

Mutual consistency of multiple visual feature maps constrains combined map topology

The primary visual cortex (V1) is the first cortical area that processes visual information relayed from the thalamus. The topologies permitted in joint ocular dominance (OD), orientation preference (OP), and direction preference (DP) maps in V1 are considered, with the aim of finding a maximally symmetric periodic case that can serve as the basis for perturbations toward natural realizations. It is shown that mutual consistency of the maps selects just two possible such lattice structures, and that one of these is much closer to experiment than the other. This comprises a hexagonal lattice of alternating positive and negative OP singularities, with each unit cell or hypercolumn containing four such singularities, each of which radiates three DP discontinuities that follow OP contours and end at OP singularities of opposite sign. Other DP discontinuities emanate at 90 degrees to the midpoints of the ones that link OP singularities, and cross OP contours perpendicularly. These features explain experimentally observed relationships between DP discontinuities and OP contours, including sudden approximately 90-degree changes of direction in the former.

Retinal waves align the concentric orientation map in mouse superior colliculus to the center of vision

Neurons in the mouse superior colliculus (SC) are arranged in a concentric orientation map, which is aligned to the center of vision and the optic flow experienced by the mouse. The origin of this map remains unclear. Here, we propose that spontaneous retinal waves during development provide a scaffold to establish the concentric orientation map within the SC and its alignment to the optic flow. We test this hypothesis by modeling the orientation-tuned SC neurons that receive ON/OFF retinal inputs. Our model suggests that the propagation direction bias of stage III retinal waves, together with OFF-delayed responses, shapes the spatial organization of the orientation map. The OFF delay establishes orientation-tuned neurons by segregating their ON/OFF receptive subfields, the wave-like activities form the concentric pattern, and the direction biases align the map to the center of vision. Together, retinal waves may play an instructive role in establishing functional properties of single SC neurons and their spatial organization within maps.

Sustained attention required for effective dimension-based retro-cue benefit in visual working memory

In visual working memory (VWM) tasks, participants' performances can be improved through the use of dimension-based retro-cues, which direct internal attention to prioritize a particular dimension (e.g., color or orientation) of VWM representations even after the stimuli disappear. This phenomenon is known as the dimension-based retro-cue benefit (RCB). The present study investigates whether sustained attention is required for the dimension-based RCB by inserting interference or interruption between the retro-cue and the test array to distract attention. We tested the effects of perceptual interference or cognitive interruption on dimension-based RCB when the interference (Experiments 1 and 2 with masks) or interruption (Experiments 3 and 4 with an odd-even task) occurred concurrently with the stages for the maintenance of prioritized information (long cue-and-interference/interruption interstimulus interval, e.g., Experiments 1 and 3) or the deployment of attention (short cue-and-interference/interruption interstimulus interval, e.g., Experiments 2 and 4). Our results demonstrate that perceptual interference or cognitive interruption attenuates the dimension-based RCB. These findings suggest that sustained attention is necessary for the effective prioritization of a specific dimension of VWM representations.

Optimal twist angle for a graphene-like bilayer

The first optimal-or 'magic'-angle leading to the nullity of the Dirac/Fermi velocity for twisted bilayer graphene is re-evaluated in the Bistritzer-MacDonald set-up (Bistritzer and MacDonald 2011Proc. Natl Acad. Sci.10812233-7). From the details of that calculation we study the resulting alterations when the properties of the two layers are not exactly the same. A moiré combination of lattices without relative rotation but with different spacing lengths may also lead to a vanishing Dirac velocity. Hopping amplitudes can vary as well, and curvature is one of the possible causes for their change. In the case of small curvature values and situations dominated by hopping energy scales, the optimal angle becomes wider than in the 'flat' case.

NILRNN: A Neocortex-Inspired Locally Recurrent Neural Network for Unsupervised Feature Learning in Sequential Data

Unsupervised feature learning refers to the problem of learning useful feature extraction functions from unlabeled data. Despite the great success of deep learning networks in this task in recent years, both for static and for sequential data, these systems can in general still not compete with the high performance of our brain at learning to extract useful representations from its sensory input. We propose the Neocortex-Inspired Locally Recurrent Neural Network: a new neural network for unsupervised feature learning in sequential data that brings ideas from the structure and function of the neocortex to the well-established fields of machine learning and neural networks. By mimicking connection patterns in the feedforward circuits of the neocortex, our system tries to generalize some of the ideas behind the success of convolutional neural networks to types of data other than images. To evaluate the performance of our system at extracting useful features, we have trained different classifiers using those and other learnt features as input and we have compared the obtained accuracies. Our system has shown to outperform other shallow feature learning systems in this task, both in terms of the accuracies achieved and in terms of how fast the classification task is learnt. The results obtained confirm our system as a state-of-the-art shallow feature learning system for sequential data, and suggest that extending it to or integrating it into deep architectures may lead to new successful networks that are competent at dealing with complex sequential tasks.

Thalamocortical boutons cluster by ON/OFF responses in mouse primary visual cortex

In higher mammals, the thalamic afferents to primary visual cortex cluster according to their responses to increases (ON) or decreases (OFF) in luminance. This feature of thalamocortical wiring is thought to create columnar, ON/OFF domains in V1. We have recently shown that mice also have ON/OFF cortical domains, but the organization of their thalamic afferents remains unknown. Here we measured the visual responses of thalamocortical boutons with two-photon imaging and found that they also cluster in space according to ON/OFF responses. Moreover, fluctuations in the relative density of ON/OFF boutons mirror fluctuations in the relative density of ON/OFF receptive field positions on the visual field. These findings indicate a segregation of ON/OFF signals already present in the thalamic input. We propose that ON/OFF clustering may reflect the spatial distribution of ON/OFF responses in retinal ganglion cell mosaics.NEW & NOTEWORTHY Neurons in primary visual cortex cluster into ON and OFF domains, which have been shown to be linked to the organization of receptive fields and cortical maps. Here we show that in the mouse such clustering is already present in the geniculate input, suggesting that the cortical architecture may be shaped by the representation of ON/OFF signals in the thalamus and the retina.

The large numbers of minicolumns in the primary visual cortex of humans, chimpanzees and gorillas are related to high visual acuity

Minicolumns are thought to be a fundamental neural unit in the neocortex and their replication may have formed the basis of the rapid cortical expansion that occurred during primate evolution. We sought evidence of minicolumns in the primary visual cortex (V-1) of three great apes, three rodents and representatives from three other mammalian orders: Eulipotyphla (European hedgehog), Artiodactyla (domestic pig) and Carnivora (ferret). Minicolumns, identified by the presence of a long bundle of radial, myelinated fibers stretching from layer III to the white matter of silver-stained sections, were found in the human, chimpanzee, gorilla and guinea pig V-1. Shorter bundles confined to one or two layers were found in the other species but represent modules rather than minicolumns. The inter-bundle distance, and hence density of minicolumns, varied systematically both within a local area that might represent a hypercolumn but also across the whole visual field. The distance between all bundles had a similar range for human, chimpanzee, gorilla, ferret and guinea pig: most bundles were 20-45 μm apart. By contrast, the space between bundles was greater for the hedgehog and pig (20-140 μm). The mean density of minicolumns was greater in tangential sections of the gorilla and chimpanzee (1,243-1,287 bundles/mm2) than in human (314-422 bundles/mm2) or guinea pig (643 bundles/mm2). The minicolumnar bundles did not form a hexagonal lattice but were arranged in thin curving and branched bands separated by thicker bands of neuropil/somata. Estimates of the total number of modules/minicolumns within V-1 were strongly correlated with visual acuity.

Invariance of object detection in untrained deep neural networks

The ability to perceive visual objects with various types of transformations, such as rotation, translation, and scaling, is crucial for consistent object recognition. In machine learning, invariant object detection for a network is often implemented by augmentation with a massive number of training images, but the mechanism of invariant object detection in biological brains—how invariance arises initially and whether it requires visual experience—remains elusive. Here, using a model neural network of the hierarchical visual pathway of the brain, we show that invariance of object detection can emerge spontaneously in the complete absence of learning. First, we found that units selective to a particular object class arise in randomly initialized networks even before visual training. Intriguingly, these units show robust tuning to images of each object class under a wide range of image transformation types, such as viewpoint rotation. We confirmed that this “innate” invariance of object selectivity enables untrained networks to perform an object-detection task robustly, even with images that have been significantly modulated. Our computational model predicts that invariant object tuning originates from combinations of non-invariant units via random feedforward projections, and we confirmed that the predicted profile of feedforward projections is observed in untrained networks. Our results suggest that invariance of object detection is an innate characteristic that can emerge spontaneously in random feedforward networks.

Natural scene sampling reveals reliable coarse-scale orientation tuning in human V1

Orientation selectivity in primate visual cortex is organized into cortical columns. Since cortical columns are at a finer spatial scale than the sampling resolution of standard BOLD fMRI measurements, analysis approaches have been proposed to peer past these spatial resolution limitations. It was recently found that these methods are predominantly sensitive to stimulus vignetting - a form of selectivity arising from an interaction of the oriented stimulus with the aperture edge. Beyond vignetting, it is not clear whether orientation-selective neural responses are detectable in BOLD measurements. Here, we leverage a dataset of visual cortical responses measured using high-field 7T fMRI. Fitting these responses using image-computable models, we compensate for vignetting and nonetheless find reliable tuning for orientation. Results further reveal a coarse-scale map of orientation preference that may constitute the neural basis for known perceptual anisotropies. These findings settle a long-standing debate in human neuroscience, and provide insights into functional organization principles of visual cortex.

Pooling strategies in V1 can account for the functional and structural diversity across species

Neurons in the primary visual cortex are selective to orientation with various degrees of selectivity to the spatial phase, from high selectivity in simple cells to low selectivity in complex cells. Various computational models have suggested a possible link between the presence of phase invariant cells and the existence of orientation maps in higher mammals’ V1. These models, however, do not explain the emergence of complex cells in animals that do not show orientation maps. In this study, we build a theoretical model based on a convolutional network called Sparse Deep Predictive Coding (SDPC) and show that a single computational mechanism, pooling, allows the SDPC model to account for the emergence in V1 of complex cells with or without that of orientation maps, as observed in distinct species of mammals. In particular, we observed that pooling in the feature space is directly related to the orientation map formation while pooling in the retinotopic space is responsible for the emergence of a complex cells population. Introducing different forms of pooling in a predictive model of early visual processing as implemented in SDPC can therefore be viewed as a theoretical framework that explains the diversity of structural and functional phenomena observed in V1.

ON/OFF domains shape receptive field structure in mouse visual cortex

In higher mammals, thalamic afferents to primary visual cortex (area V1) segregate according to their responses to increases (ON) or decreases (OFF) in luminance. This organization induces columnar, ON/OFF domains postulated to provide a scaffold for the emergence of orientation tuning. To further test this idea, we asked whether ON/OFF domains exist in mouse V1. Here we show that mouse V1 is indeed parceled into ON/OFF domains. Interestingly, fluctuations in the relative density of ON/OFF neurons on the cortical surface mirror fluctuations in the relative density of ON/OFF receptive field centers on the visual field. Moreover, the local diversity of cortical receptive fields is explained by a model in which neurons linearly combine a small number of ON and OFF signals available in their cortical neighborhoods. These findings suggest that ON/OFF domains originate in fluctuations of the balance between ON/OFF responses across the visual field which, in turn, shapes the structure of cortical receptive fields.

A theory of cortical map formation in the visual brain

The cerebral cortex receives multiple afferents from the thalamus that segregate by stimulus modality forming cortical maps for each sense. In vision, the primary visual cortex maps the multiple dimensions of the visual stimulus in patterns that vary across species for reasons unknown. Here we introduce a general theory of cortical map formation, which proposes that map diversity emerges from species variations in the thalamic afferent density sampling sensory space. In the theory, increasing afferent sampling density enlarges the cortical domains representing the same visual point, allowing the segregation of afferents and cortical targets by multiple stimulus dimensions. We illustrate the theory with an afferent-density model that accurately replicates the maps of different species through afferent segregation followed by thalamocortical convergence pruned by visual experience. Because thalamocortical pathways use similar mechanisms for axon segregation and pruning, the theory may extend to other sensory areas of the mammalian brain.

Mechanism underpinning the sharpening of orientation and spatial frequency selectivities in the tree shrew (Tupaia belangeri) primary visual cortex

Most neurons in the primary visual cortex (V1) of mammals show sharp orientation selectivity and band-pass spatial frequency tuning. Here, we examine whether sharpening of the broad tuning that exists subcortically, namely in the retina and the lateral geniculate nucleus (LGN), underlie the sharper tuning seen for both the above features in tree shrew V1. Since the transition from poor feature selectivity to sharp tuning occurs entirely within V1 in tree shrews, we examined the orientation selectivity and spatial frequency tuning of neurons within individual electrode penetrations. We found that most layer 4 and layer 2/3 neurons in the same cortical column preferred the same stimulus orientation. However, a subset of layer 3c neurons close to the layer 4 border preferred near orthogonal orientations, suggesting that layer 2/3 neurons may inherit the orientation preferences of their layer 4 input neurons and also receive cross-orientation inhibition from layer 3c neurons. We also found that layer 4 neurons showed sharper orientation selectivity at higher spatial frequencies, suggesting that attenuation of low spatial frequency responses by spatially broad inhibition acting on layer 4 inputs to layer 2/3 neurons can enhance both orientation and spatial frequency selectivities. However, in a proportion of layer 2/3 neurons, the sharper tuning of layer 2/3 neurons appeared to arise also or even mainly from inhibition specific to high spatial frequencies acting on the layer 4 inputs to layer 2/3. Overall, our results are consistent with the suggestion that in tree shrews, sharp feature selectivity in layer 2/3 can be established by intracortical mechanisms that sharpen biases observed in layer 4, which are in turn inherited presumably from thalamic afferents.

Face detection in untrained deep neural networks

Face-selective neurons are observed in the primate visual pathway and are considered as the basis of face detection in the brain. However, it has been debated as to whether this neuronal selectivity can arise innately or whether it requires training from visual experience. Here, using a hierarchical deep neural network model of the ventral visual stream, we suggest a mechanism in which face-selectivity arises in the complete absence of training. We found that units selective to faces emerge robustly in randomly initialized networks and that these units reproduce many characteristics observed in monkeys. This innate selectivity also enables the untrained network to perform face-detection tasks. Intriguingly, we observed that units selective to various non-face objects can also arise innately in untrained networks. Our results imply that the random feedforward connections in early, untrained deep neural networks may be sufficient for initializing primitive visual selectivity.

Periodic clustering of simple and complex cells in visual cortex

Neurons in the primary visual cortex (V1) are often classified as simple or complex cells, but it is debated whether they are discrete hierarchical classes of neurons or if they represent a continuum of variation within a single class of cells. Herein, we show that simple and complex cells may arise commonly from the feedforward projections from the retina. From analysis of the cortical receptive fields in cats, we show evidence that simple and complex cells originate from the periodic variation of ON-OFF segregation in the feedforward projection of retinal mosaics, by which they organize into periodic clusters in V1. From data in cats, we observed that clusters of simple and complex receptive fields correlate topographically with orientation maps, which supports our model prediction. Our results suggest that simple and complex cells are not two distinct neural populations but arise from common retinal afferents, simultaneous with orientation tuning.

Sparse thalamocortical convergence

How many thalamic neurons converge onto a cortical cell? This is an important question, because the organization of thalamocortical projections can influence the cortical architecture.^1,2 Here, we estimate the degree of thalamocortical convergence in primary visual cortex by taking advantage of the cortical expansion-neurons within a restricted volume in primary visual cortex have overlapping receptive fields driven by a smaller set of inputs from the lateral geniculate nucleus.^3-5 Under these conditions, the measurements of cortical receptive fields in a population can be used to infer the receptive fields of their geniculate inputs and the weights of their projections using non-negative matrix factorization.⁶ The analysis reveals sparse connectivity,⁷ where a handful (~2-6) of thalamic inputs account for 90% of the total synaptic weight to a cortical neuron. Together with previous findings,⁸ these results paint a picture consistent with the idea that convergence of a few inputs partly determine the retinotopy and tuning properties of cortical cells.^8-13.

Precise Mapping of Single Neurons by Calibrated 3D Reconstruction of Brain Slices Reveals Topographic Projection in Mouse Visual Cortex

Recent breakthroughs in neuroanatomical tracing methods have helped unravel complicated neural connectivity in whole-brain tissue at single-cell resolution. However, in most cases, analysis of brain images remains dependent on highly subjective and sample-specific manual processing, preventing precise comparison across sample animals. In the present study, we introduce AMaSiNe, software for automated mapping of single neurons in the standard mouse brain atlas with annotated regions. AMaSiNe automatically calibrates misaligned and deformed slice samples to locate labeled neuronal positions from multiple brain samples into the standardized 3D Allen Mouse Brain Reference Atlas. We exploit the high fidelity and reliability of AMaSiNe to investigate the topographic structures of feedforward projections from the lateral geniculate nucleus to the primary visual area by reconstructing rabies-virus-injected brain slices in 3D space. Our results demonstrate that distinct organization of neural projections can be precisely mapped using AMaSiNe.

Retino-Cortical Mapping Ratio Predicts Columnar and Salt-and-Pepper Organization in Mammalian Visual Cortex

In the mammalian primary visual cortex, neural tuning to stimulus orientation is organized in either columnar or salt-and-pepper patterns across species. For decades, this sharp contrast has spawned fundamental questions about the origin of functional architectures in visual cortex. However, it is unknown whether these patterns reflect disparate developmental mechanisms across mammalian taxa or simply originate from variation of biological parameters under a universal development process. In this work, after the analysis of data from eight mammalian species, we show that cortical organization is predictable by a single factor, the retino-cortical mapping ratio. Groups of species with or without columnar clustering are distinguished by the feedforward sampling ratio, and model simulations with controlled mapping conditions reproduce both types of organization. Prediction from the Nyquist theorem explains this parametric division of the patterns with high accuracy. Our results imply that evolutionary variation of physical parameters may induce development of distinct functional circuitry.

Real-world objects are not stored in holistic representations in visual working memory

When storing multiple objects in visual working memory, observers sometimes misattribute perceived features to incorrect locations or objects. These misattributions are called binding errors (or swaps) and have been previously demonstrated mostly in simple objects whose features are easy to encode independently and arbitrarily chosen, like colors and orientations. Here, we tested whether similar swaps can occur with real-world objects, where the connection between features is meaningful rather than arbitrary. In Experiments 1 and 2, observers were simultaneously shown four items from two object categories. Within a category, the two exemplars could be presented in either the same or different states (e.g., open/closed; full/empty). After a delay, both exemplars from one of the categories were probed, and participants had to recognize which exemplar went with which state. We found good memory for state information and exemplar information on their own, but a significant memory decrement for exemplar-state combinations, suggesting that binding was difficult for observers and swap errors occurred even for meaningful real-world objects. In Experiment 3, we used the same task, but in one-half of the trials, the locations of the exemplars were swapped at test. We found that there are more errors in general when the locations of exemplars were swapped. We concluded that the internal features of real-world objects are not perfectly bound in working memory, and location updates impair object and feature representations. Overall, we provide evidence that even real-world objects are not stored in an entirely unitized format in working memory.

Projection of Orthogonal Tiling from the Retina to the Visual Cortex

In higher mammals, the primary visual cortex (V1) is organized into diverse tuning maps of visual features. The topography of these maps intersects orthogonally, but it remains unclear how such a systematic relationship can develop. Here, we show that the orthogonal organization already exists in retinal ganglion cell (RGC) mosaics, providing a blueprint of the organization in V1. From analysis of the RGC mosaics data in monkeys and cats, we find that the ON-OFF RGC distance and ON-OFF angle of neighboring RGCs are organized into a topographic tiling across mosaics, analogous to the orthogonal intersection of cortical tuning maps. Our model simulation shows that the ON-OFF distance and angle in RGC mosaics correspondingly initiate ocular dominance/spatial frequency tuning and orientation tuning, resulting in the orthogonal intersection of cortical tuning maps. These findings suggest that the regularly structured ON-OFF patterns mirrored from the retina initiate the uniform representation of combinations of map features over the visual space.

Visual number sense in untrained deep neural networks

Number sense, the ability to estimate numerosity, is observed in naïve animals, but how this cognitive function emerges in the brain remains unclear. Here, using an artificial deep neural network that models the ventral visual stream of the brain, we show that number-selective neurons can arise spontaneously, even in the complete absence of learning. We also show that the responses of these neurons can induce the abstract number sense, the ability to discriminate numerosity independent of low-level visual cues. We found number tuning in a randomly initialized network originating from a combination of monotonically decreasing and increasing neuronal activities, which emerges spontaneously from the statistical properties of bottom-up projections. We confirmed that the responses of these number-selective neurons show the single- and multineuron characteristics observed in the brain and enable the network to perform number comparison tasks. These findings provide insight into the origin of innate cognitive functions.

Reconstruction of natural images from responses of primate retinal ganglion cells

The visual message conveyed by a retinal ganglion cell (RGC) is often summarized by its spatial receptive field, but in principle also depends on the responses of other RGCs and natural image statistics. This possibility was explored by linear reconstruction of natural images from responses of the four numerically-dominant macaque RGC types. Reconstructions were highly consistent across retinas. The optimal reconstruction filter for each RGC - its visual message - reflected natural image statistics, and resembled the receptive field only when nearby, same-type cells were included. ON and OFF cells conveyed largely independent, complementary representations, and parasol and midget cells conveyed distinct features. Correlated activity and nonlinearities had statistically significant but minor effects on reconstruction. Simulated reconstructions, using linear-nonlinear cascade models of RGC light responses that incorporated measured spatial properties and nonlinearities, produced similar results. Spatiotemporal reconstructions exhibited similar spatial properties, suggesting that the results are relevant for natural vision.

Vision Changes the Cellular Composition of Binocular Circuitry during the Critical Period

High acuity stereopsis emerges during an early postnatal critical period when binocular neurons in the primary visual cortex sharpen their receptive field tuning properties. We find that this sharpening is achieved by dismantling the binocular circuit present at critical period onset and building it anew. Longitudinal imaging of receptive field tuning (e.g., orientation selectivity) of thousands of neurons reveals that most binocular neurons present in layer 2/3 at critical period onset are poorly tuned and are rendered monocular. In parallel, new binocular neurons are established by conversion of well-tuned monocular neurons as they gain matched input from the other eye. These improvements in binocular tuning in layer 2/3 are not inherited from layer 4 but are driven by the experience-dependent sharpening of ipsilateral eye responses. Thus, vision builds a new and more sharply tuned binocular circuit in layer 2/3 by cellular exchange and not by refining the original circuit.

Spatial organization of functional clusters representing reward and movement information in the striatal direct and indirect pathways

To obtain insights into striatal neural processes underlying reward-based learning and movement control, we examined spatial organizations of striatal neurons related to movement and reward-based learning. For this, we recorded the activity of direct- and indirect-pathway neurons (D1 and A2a receptor-expressing neurons, respectively) in mice engaged in probabilistic classical conditioning and open-field free exploration. We found broadly organized functional clusters of striatal neurons in the direct as well as indirect pathways for both movement- and reward-related variables. Functional clusters for different variables were partially overlapping in both pathways, but the overlap between outcome- and value-related functional clusters was greater in the indirect than direct pathway. Also, value-related spatial clusters were progressively refined during classical conditioning. Our study shows the broad and learning-dependent spatial organization of functional clusters of dorsal striatal neurons in the direct and indirect pathways. These findings further argue against the classic model of the basal ganglia and support the importance of spatiotemporal patterns of striatal neuronal ensemble activity in the control of behavior.

Two Is Greater Than One: Binocular Visual Experience Drives Cortical Orientation Map Alignment

One hallmark of the mature visual cortex is binocularly matched orientation maps. In this issue of Neuron, Chang et al. (2020) show that three different maps exist at vision onset and that binocular visual experience aligns them into a single unified representation.

Spontaneous Retinal Waves Can Generate Long-Range Horizontal Connectivity in Visual Cortex

In the primary visual cortex (V1) of higher mammals, long-range horizontal connections (LHCs) are observed to develop, linking iso-orientation domains of cortical tuning. It is unknown how this feature-specific wiring of circuitry develops before eye-opening. Here, we suggest that LHCs in V1 may originate from spatiotemporally structured feedforward activities generated from spontaneous retinal waves. Using model simulations based on the anatomy and observed activity patterns of the retina, we show that waves propagating in retinal mosaics can initialize the wiring of LHCs by coactivating neurons of similar tuning, whereas equivalent random activities cannot induce such organizations. Simulations showed that emerged LHCs can produce the patterned activities observed in V1, matching the topography of the underlying orientation map. The model can also reproduce feature-specific microcircuits in the salt-and-pepper organizations found in rodents. Our results imply that early peripheral activities contribute significantly to cortical development of functional circuits.SIGNIFICANCE STATEMENT Long-range horizontal connections (LHCs) in the primary visual cortex (V1) are observed to emerge before the onset of visual experience, thereby selectively connecting iso-domains of orientation map. However, it is unknown how such feature-specific wirings develop before eye-opening. Here, we show that LHCs in V1 may originate from the feature-specific activation of cortical neurons by spontaneous retinal waves during early developmental stages. Our simulations of a visual cortex model show that feedforward activities from the retina initialize the spatial organization of activity patterns in V1, which induces visual feature-specific wirings in the V1 neurons. Our model also explains the origin of cortical microcircuits observed in rodents, suggesting that the proposed developmental mechanism is universally applicable to circuits of various mammalian species.

The growth of cognition: Free energy minimization and the embryogenesis of cortical computation

The assumption that during cortical embryogenesis neurons and synaptic connections are selected to form an ensemble maximising synchronous oscillation explains mesoscopic cortical development, and a mechanism for cortical information processing is implied by consistency with the Free Energy Principle and Dynamic Logic. A heteroclinic network emerges, with stable and unstable fixed points of oscillation corresponding to activity in symmetrically connected, versus asymmetrically connected, sets of neurons. Simulations of growth explain a wide range of anatomical observations for columnar and non-columnar cortex, superficial patch connections, and the organization and dynamic interactions of neurone response properties. An antenatal scaffold is created, upon which postnatal learning can establish continuously ordered neuronal representations, permitting matching of co-synchronous fields in multiple cortical areas to solve optimization problems as in Dynamic Logic. Fast synaptic competition partitions equilibria, minimizing "the curse of dimensionality", while perturbations between imperfectly partitioned synchronous fields, under internal reinforcement, enable the cortex to become adaptively self-directed. As learning progresses variational free energy is minimized and entropy bounded.

Origins of Functional Organization in the Visual Cortex

How are the complex maps for orientation selectivity (OS) created in the primary visual cortex (V1)? Rodents and rabbits have a random distribution of OS preferences across V1 while in cats, ferrets, and all primates cells with similar OS preferences cluster together into relatively wide cortical columns. Given other clear similarities in the organization of the visual pathways, why is it that maps coding OS preferences are so radically different? Prominent models have been created of cortical OS mapping that incorporate Hebbian plasticity, intracortical interactions, and the properties of growing axons. However, these models suggest that the maps arise primarily through intracortical interactions. Here we focus on several other features of the visual system and brain that may influence V1 structure. These are: eye divergence, the total number of cells in V1, the thalamocortical networks, the topography of the retina and phylogeny. We outline the evidence for and against these factors contributing to map formation. One promising theory is that the central-to-peripheral ratio (CP ratio) of retinal cell density can be used to predict whether or not a species has pinwheel maps. Animals with high CP ratios (>7) have orientation columns while those with low CP ratios (<4) have random OS maps. The CP ratio is related to the total number of cells in cortex, which also appears to be a reasonable contributing factor. However, while these factors correlate with map structure to some extent, there is a gray area where certain species do not fit elegantly into the theory. A problem with the existing literature is that OS maps have been investigated in only a small number of mammals, from a small fraction of the mammalian phylogenetic tree. We suggest four species (agouti, fruit bat, sheep, and wallaby) that have a range of interesting characteristics, which sit at intermediate locations between primates and rodents, that make them good targets for filling in the missing gaps in the literature. We make predictions about the map structures of these species based on the organization of their brains and visual systems and, in doing so, set possible paths for future research.

Distinct roles of parvalbumin- and somatostatin-expressing neurons in flexible representation of task variables in the prefrontal cortex

A hallmark of the prefrontal cortex (PFC) is flexible representation of task-relevant variables. To investigate roles of different interneuron subtypes in this process, we examined discharge characteristics and inactivation effects of parvalbumin (PV)- and somatostatin (SST)-expressing neurons in the mouse PFC during probabilistic classical conditioning. We found activity patterns and inactivation effects differed between PV and SST neurons: SST neurons conveyed cue-associated quantitative value signals until trial outcome, whereas PV neurons maintained valence signals even after trial outcome. Also, PV, but not SST, neuronal population showed opposite responses to reward and punishment. Moreover, inactivation of PV, but not SST, neurons affected outcome responses and activity reversal of pyramidal neurons. Modeling suggested opposite responses of PV neurons to reward and punishment as an efficient mechanism for facilitating rapid cue-outcome contingency learning. Our results suggest primary roles of mPFC PV neurons in rapid value updating and SST neurons in predicting values of upcoming events.

Diversity of Ocular Dominance Patterns in Visual Cortex Originates from Variations in Local Cortical Retinotopy

The primary visual cortex contains a detailed map of retinal stimulus position (retinotopic map) and eye input (ocular dominance map) that results from the precise arrangement of thalamic afferents during cortical development. For reasons that remain unclear, the patterns of ocular dominance are very diverse across species and can take the shape of highly organized stripes, convoluted beads, or no pattern at all. Here, we use a new image-processing algorithm to measure ocular dominance patterns more accurately than in the past. We use these measurements to demonstrate that ocular dominance maps follow a common organizing principle that makes the cortical axis with the slowest retinotopic gradient orthogonal to the ocular dominance stripes. We demonstrate this relation in multiple regions of the primary visual cortex from individual animals, and different species. Moreover, consistent with the increase in the retinotopic gradient with visual eccentricity, we demonstrate a strong correlation between eccentricity and ocular dominance stripe width. We also show that an eye/polarity grid emerges within the visual cortical map when the representation of light and dark stimuli segregates along an axis orthogonal to the ocular dominance stripes, as recently demonstrated in cats. Based on these results, we propose a developmental model of visual cortical topography that sorts thalamic afferents by eye input and stimulus polarity, and then maximizes the binocular retinotopic match needed for depth perception and the light-dark retinotopic mismatch needed to process stimulus orientation. In this model, the different ocular dominance patterns simply emerge from differences in local retinotopic cortical topography.SIGNIFICANCE STATEMENT Thalamocortical afferents segregate in primary visual cortex by eye input and light-dark polarity. This afferent segregation forms cortical patterns that vary greatly across species for reasons that remain unknown. Here we show that the formation of ocular dominance patterns follows a common organizing principle across species that aligns the cortical axis of ocular dominance segregation with the axis of slowest retinotopic gradient. Based on our results, we propose a model of visual cortical topography that sorts thalamic afferents by eye input and stimulus polarity along orthogonal axes with the slowest and fastest retinotopic gradients, respectively. This organization maximizes the binocular retinotopic match needed for depth perception and the light-dark retinotopic mismatch needed to process stimulus orientation in carnivores and primates.

A model for the origin and development of visual orientation selectivity

Orientation selectivity is a key property of primary visual cortex that contributes, downstream, to object recognition. The origin of orientation selectivity, however, has been debated for decades. It is known that on- and off-centre subcortical pathways converge onto single neurons in primary visual cortex, and that the spatial offset between these pathways gives rise to orientation selectivity. On- and off-centre pathways are intermingled, however, so it is unclear how their inputs to cortex come to be spatially segregated. We here describe a model in which the segregation occurs through Hebbian strengthening and weakening of geniculocortical synapses during the development of the visual system. Our findings include the following. 1. Neighbouring on- and off-inputs to cortex largely cancelled each other at the start of development. At each receptive field location, the Hebbian process increased the strength of one input sign at the expense of the other sign, producing a spatial segregation of on- and off-inputs. 2. The resulting orientation selectivity was precise in that the bandwidths of the orientation tuning functions fell within empirical estimates. 3. The model produced maps of preferred orientation–complete with iso-orientation domains and pinwheels–similar to those found in real cortex. 4. These maps did not originate in cortical processes, but from clustering of off-centre subcortical pathways and the relative location of neighbouring on-centre clusters. We conclude that a model with intermingled on- and off-pathways shaped by Hebbian synaptic plasticity can explain both the origin and development of orientation selectivity.

Many neurons in mammalian primary visual cortex are highly selective for the orientation of visual contours and can therefore contribute to object recognition. Orientation selectivity depends on on- and off-centre retinal neurons that respond, respectively, to light and dark. We describe a signal-processing model that includes both subcortical pathways and cortical neurons. The model predicts the preferred orientation of a cortical neuron from the empirically determined spatial layout of retinal cells. Further, the subcortical-to-cortical connections change in strength during visual development, meaning that cortical neurons in the model have orientation selectivity just as precise as real neurons. Our model can therefore explain the origin of orientation selectivity and the way it develops during visual system maturation.

Spinodal decomposition in a mean-field model of the cortex: Emergence of hexagonally symmetric activation patterns

Spinodal decomposition is a well-known pattern-forming mechanism in metallurgic alloys, semiconductor crystals, and colloidal gels. In metallurgy, if a heated sample of a homogeneous Zn-Al alloy is suddenly quenched below a critical temperature, then the sample can spontaneously precipitate into inhomogenous textures of Zn- and Al-rich regions with significantly altered material properties such as ductility and hardness. Here we report on our recent discovery that a two-dimensional model of the human cortex with inhibitory diffusion can, under particular homogeneous initial conditions, exhibit a form of nonconserved spinodal decomposition in which regions of the cortex self-organize into hexagonally distributed binary patches of activity and inactivity. Fine-scale patterns precipitate rapidly, and then the dynamics slows to render coarser-scale shapes which can ripen into a range of slowly evolving patterns including mazelike labyrinths, hexagonal islands and continents, nucleating "mitotic cells" which grow to a critical size then subdivide, and inverse nucleations in which quiescent islands are surrounded by a sea of activity. One interesting class of activity coalesces into a soliton-like narrow ribbon of depolarization that traverses the cortex at ∼4cm/s. We speculate that this may correspond to the thus far unexplained interictal waves of cortical activation that precede grand-mal seizure in an epileptic event. We note that spinodal decomposition is quite distinct from the Turing mechanism for symmetry breaking in cortex investigated in earlier work by the authors [Steyn-Ross et al., Phys. Rev. E 76, 011916 (2007)PLEEE81539-375510.1103/PhysRevE.76.011916].

Linkage between retinal ganglion cell density and the nonuniform spatial integration across the visual field

The integration of visual information over space is critical to human pattern vision. For either luminance detection or object recognition, the position of the target in the visual field governs the size of a window within which visual information is integrated. Here we analyze the relationship between the topographic distribution of ganglion cell density and the nonuniform spatial integration across the visual field. We find that the variation in the retinal ganglion cell (RGC) density across the human retina is closely matched to the variation in the extent of spatial integration. Our study suggests that a fixed number of RGCs subserves spatial integration of visual input, independent of the visual-field location.

The ability to integrate visual information over space is a fundamental component of human pattern vision. Regardless of whether it is for detecting luminance contrast or for recognizing objects in a cluttered scene, the position of the target in the visual field governs the size of a window within which visual information is integrated. Here we analyze the relationship between the topographic distribution of ganglion cell density and the nonuniform spatial integration across the visual field. The extent of spatial integration for luminance detection (Ricco’s area) and object recognition (crowding zone) are measured at various target locations. The number of retinal ganglion cells (RGCs) underlying Ricco’s area or crowding zone is estimated by computing the product of Ricco’s area (or crowding zone) and RGC density for a given target location. We find a quantitative agreement between the behavioral data and the RGC density: The variation in the sampling density of RGCs across the human retina is closely matched to the variation in the extent of spatial integration required for either luminance detection or object recognition. Our empirical data combined with the simulation results of computational models suggest that a fixed number of RGCs subserves spatial integration of visual input, independent of the visual-field location.

The basic repeating modules of the cerebral cortical circuit

The fundamental organization of the cerebral cortical circuit is still poorly understood. In particular, it is unclear whether the diverse cell types form modular units that are repeated across the cortex. We discovered that the major cell types in cortical layer 5 form a lattice structure. Distinct types of excitatory and inhibitory neurons form cell type-specific radial clusters termed microcolumns. Microcolumns are present in diverse cortical areas, such as the visual, motor, and language areas, and are organized into periodic hexagonal lattice structures. Individual microcolumns have modular synaptic circuits and exhibit modular neuronal activity, suggesting that each of them functions as an information processing unit. Microcolumn development is suggested to be independent of cell lineage but coordinated by gap junctions. Thus, neurons in cortical layer 5 organize into a brainwide lattice structure of functional microcolumns, suggesting that parallel processing by massively repeated microcolumns underlie diverse cortical functions, such as sensory perception, motor control, and language processing.

Beyond Rehabilitation of Acuity, Ocular Alignment, and Binocularity in Infantile Strabismus

Infantile strabismus impairs the perception of all attributes of the visual scene. High spatial frequency components are no longer visible, leading to amblyopia. Binocularity is altered, leading to the loss of stereopsis. Spatial perception is impaired as well as detection of vertical orientation, the fastest movements, directions of movement, the highest contrasts and colors. Infantile strabismus also affects other vision-dependent processes such as control of postural stability. But presently, rehabilitative therapies for infantile strabismus by ophthalmologists, orthoptists and optometrists are restricted to preventing or curing amblyopia of the deviated eye, aligning the eyes and, whenever possible, preserving or restoring binocular vision during the critical period of development, i.e., before ~10 years of age. All the other impairments are thus ignored; whether they may recover after strabismus treatment even remains unknown. We argue here that medical and paramedical professionals may extend their present treatments of the perceptual losses associated with infantile strabismus. This hypothesis is based on findings from fundamental research on visual system organization of higher mammals in particular at the cortical level. In strabismic subjects (as in normal-seeing ones), information about all of the visual attributes converge, interact and are thus inter-dependent at multiple levels of encoding ranging from the single neuron to neuronal assemblies in visual cortex. Thus if the perception of one attribute is restored this may help to rehabilitate the perception of other attributes. Concomitantly, vision-dependent processes may also improve. This could occur spontaneously, but still should be assessed and validated. If not, medical and paramedical staff, in collaboration with neuroscientists, will have to break new ground in the field of therapies to help reorganize brain circuitry and promote more comprehensive functional recovery. Findings from fundamental research studies in both young and adult patients already support our hypothesis and are reviewed here. For example, presenting different contrasts to each eye of a strabismic patient during training sessions facilitates recovery of acuity in the amblyopic eye as well as of 3D perception. Recent data also demonstrate that visual recoveries in strabismic subjects improve postural stability. These findings form the basis for a roadmap for future research and clinical development to extend presently applied rehabilitative therapies for infantile strabismus.

Thalamocortical Circuits and Functional Architecture

The thalamocortical pathway is the main route of communication between the eye and the cerebral cortex. During embryonic development, thalamocortical afferents travel to L4 and are sorted by receptive field position, eye of origin, and contrast polarity (i.e., preference for light or dark stimuli). In primates and carnivores, this sorting involves numerous afferents, most of which sample a limited region of the binocular field. Devoting abundant thalamocortical resources to process a limited visual field has a clear advantage: It allows many stimulus combinations to be sampled at each spatial location. Moreover, the sampling efficiency can be further enhanced by organizing the afferents in a cortical grid for eye input and contrast polarity. We argue that thalamocortical interactions within this eye-polarity grid can be used to represent multiple stimulus combinations found in nature and to build an accurate cortical map for multidimensional stimulus space.

Thalamocortical processing in vision

Visual information reaches the cerebral cortex through a major thalamocortical pathway that connects the lateral geniculate nucleus (LGN) of the thalamus with the primary visual area of the cortex (area V1). In humans, ∼3.4 million afferents from the LGN are distributed within a V1 surface of ∼2400 mm2, an afferent number that is reduced by half in the macaque and by more than two orders of magnitude in the mouse. Thalamocortical afferents are sorted in visual cortex based on the spatial position of their receptive fields to form a map of visual space. The visual resolution within this map is strongly correlated with total number of thalamic afferents that V1 receives and the area available to sort them. The ∼20,000 afferents of the mouse are only sorted by spatial position because they have to cover a large visual field (∼300 deg) within just 4 mm2 of V1 area. By contrast, the ∼500,000 afferents of the cat are also sorted by eye input and light/dark polarity because they cover a smaller visual field (∼200 deg) within a much larger V1 area (∼400 mm2), a sorting principle that is likely to apply also to macaques and humans. The increased precision of thalamic sorting allows building multiple copies of the V1 visual map for left/right eyes and light/dark polarities, which become interlaced to keep neurons representing the same visual point close together. In turn, this interlaced arrangement makes cortical neurons with different preferences for stimulus orientation to rotate around single cortical points forming a pinwheel pattern that allows more efficient processing of objects and visual textures.

Distribution and diversity of intrinsically photosensitive retinal ganglion cells in tree shrew

Intrinsically photosensitive retinal ganglion cells (ipRGCs) mediate the pupillary light reflex, circadian entrainment, and may contribute to luminance and color perception. The diversity of ipRGCs varies from rodents to primates, suggesting differences in their contributions to retinal output. To further understand the variability in their organization and diversity across species, we used immunohistochemical methods to examine ipRGCs in tree shrew (Tupaia belangeri). Tree shrews share membership in the same clade, or evolutionary branch, as rodents and primates. They are highly visual, diurnal animals with a cone-dominated retina and a geniculo-cortical organization resembling that of primates. We identified cells with morphological similarities to M1 and M2 cells described previously in rodents and primates. M1-like cells typically had somas in the ganglion cell layer, with 23% displaced to the inner nuclear layer (INL). However, unlike M1 cells, they had bistratified dendritic fields ramifying in S1 and S5 that collectively tiled space. M2-like cells had dendritic fields restricted to S5 that were smaller and more densely branching. A novel third type of melanopsin immunopositive cell was identified. These cells had somata exclusively in the INL and monostratified dendritic fields restricted to S1 that tiled space. Surprisingly, these cells immunolabeled for tyrosine hydroxylase, a key component in dopamine synthesis. These cells immunolabeled for an RGC marker, not amacrine cell markers, suggesting that they are dopaminergic ipRGCs. We found no evidence for M4 or M5 ipRGCs, described previously in rodents. These results identify some organizational features of the ipRGC system that are canonical versus species-specific.