Objectives:To quickly characterize multifocal Pupillary Response Fields (mPRF) using frequency tagging and to identify pupillary biomarkers of age-related macular degeneration (AMD) and diabetic retinopathy (DR). Methods:Participants with AMD (N = 74), DR (N = 56), and healthy controls (HP, N = 62) underwent standard ophthalmologic assessments together with a multifocal Pupillary Frequency Tagging test (mPFT). The mPFT test comprised 9 retinal regions whose luminance were sinusoidally modulated at incommensurate temporal frequencies so as to elicit sustained pupillary oscillations over 45 s of fixation. Analyses:The recorded pupillary traces were corrected for blinks and transient artifacts. Features of pupillary dynamics and eye-movements, - eye instability during fixation, pupil light reflex, and spectral components of pupil oscillations - were compared across groups. Statistical analyses were performed for each feature separately using Student t-tests and Cohen's d. The Area under the Curve (AUC) of the Receiver Operating Characteristics (ROC) was computed for different combinations of the extracted features. Pearson's correlation was used to compare spectral power with other functional measures. Results:Multifocal Pupillary Response Fields (mPRF) derived from regional spectral power and phase distributions differed significantly between patients and controls, in accordance with the characteristics of each pathology: decreased power for central and paracentral sectors in AMD, diffuse defects in DR. AUCs of ROC performed with relevant features achieved excellent sensitivity (>0.9) and specificity (>0.9) in classifying patients from healthy subjects. Conclusion:Fast, objective, and easily recorded, mPRF assessments evaluate the functional integrity of retino-pupillary circuits, providing spatiotemporal bio-signatures selective for maculopathies and retinopathies.

Pupil cycle time (PCT) estimates the dynamics of a biofeedback loop established between pupil size and stimulus luminance, size or colour. The PCT is useful for probing the functional integrity of the retinopupillary circuits, and is therefore potentially applicable for assessing the effects of damage due to retinopathies or neuropathies. In previous studies, PCT was measured by manually counting the number of pupil oscillations during a fixed period to calculate the PCT. This method is scarce, requires a good expertise and cannot be used to estimate several PCT parameters, such as the oscillation amplitude or variability. We have developed a computerised setup based on eye-tracking that expands the possibilities of characterising PCT along several dimensions: oscillation frequency and regularity, amplitude and variability, which can be used with a large palette of stimuli (different colours, sizes, shapes or locations), and further allows measuring blinking frequency and eye movements. We used this method to characterise the PCT in young control participants as well as in patients with several pathologies, including age-related macular degeneration (AMD), diabetic retinopathy (DR), retinitis pigmentosa (RP), Stargardt disease (SD), and Leber hereditary optic neuropathy (LHON). We found that PCT is very regular and stable in young healthy participants, with little inter-individual variability. In contrast, several PCT features are altered in older healthy participants as well as in ocular diseases, including slower dynamics, irregular oscillations, and reduced oscillation amplitude. The distinction between patients and healthy participants based on the calculation of the area under the curve of the receiver operating characteristics (AUC of ROC) were dependent on the pathologies and stimuli (0.7 < AUC < 1). PCT nevertheless provides relevant complementary information to assess the physiopathology of ocular diseases and to probe the functioning of retino-pupillary circuits.

Non-stationary dynamical cortical states − neural activity changing on the topology of the cortex across time − and in particular traveling waves, is an emerging topic. In this article, we propose that similar spatio-temporal traveling wave patterns observed across cortical scales are underpinned by generative mechanisms that differ in nature, that we categorize as first- and second-order traveling waves. This original definition provides a unifying framework making testable predictions at both mechanistic and functional levels. While having diverse mechanistic origins, we propose that traveling waves across spatial and temporal scales subserve a canonical computation at the core of a variety of brain functions, thereby ordering neuronal processing to impose a computational syntax.

BACKGROUND:Marmosets are becoming an increasingly important animal model in Neuroscience and optical approaches such as two-photon microscopy are expected to provide data significantly contributing to our understanding of the brain, especially when performed chronically. NEW METHOD:We describe a novel imaging chamber that provides an optical window onto the marmoset cerebral cortex over several months, together with the surgery needed for its implantation and that of an associated headpost. MRI data allow optimal positioning on the skull. The chamber is tightly inserted into a craniotomy and its sealing system combines a thread with a silicone elastomer minimizing infection risk and CSF leakage. Continuous contact between the window and the cortex delays tissue regrowth. Opening and sterile re-sealing are easy to perform whenever access to the cortex is needed. RESULTS:Functional data from longitudinal two-photon imaging using genetically encoded fluorescent calcium sensors reveal high optical quality over months. We also demonstrate the feasibility of two-photon imaging of genetically encoded voltage sensors in the marmoset cortex in-vivo. COMPARISON WITH EXISTING METHODS:The chamber provides a field of view twice as large as current designs yet can be machined with standard equipment. Using a curved rather than flat glass window together with easy adjustment of the window's distance with respect to the underlying cortex allows achieving gentle but continuous contact with the underlying cortex. CONCLUSIONS:Currently, our chamber already provides a durable solution for long-term imaging in marmosets. Yet, we discuss a few straightforward modifications that may improve its performance even further.

The flash-lag effect (FLE) is an illusion whereby the position of a moving object is perceived as being offset in the direction of movement when compared to a flashed static stimulus. This perceptual misalignment has been posited as a key phenomenon in explaining our ability to accurately predict the future position of moving objects, despite the delays in neuronal processing. Our working hypothesis is that the FLE is resulting from the anticipation generated by propagation of neural activity within visual cortical retinotopical maps. According to this hypothesis, the FLE should be affected by discontinuities and anisotropies of the retinotopic map architecture. Using psychophysics we show that the FLE is strongly affected by crossing and the direction of motion in respect to retinotopic features, such as vertical and horizontal meridians and the fovea. The specificity of how early visual cortical retinotopic maps are splitted and magnified around these features led us to suggest that the FLE distortions emerge from propagation in retinotopically organized networks, particularly V1. This work bridges the gap between human psychophysics and the known constraints of retinotopic maps layout, offering a testable framework for future studies of motion position encoding in the visual hierarchy. ### Competing Interest Statement The authors have declared no competing interest. European Unionșs Horizon 2020, Marie Skłodowska-Curie, 956669 Fondation de France, https://ror.org/02zkxjz73

We propose a mean field model of the primary visual cortex (V1), connected to a realistic retina model, to study the impact of the retina on motion anticipation. We first consider the case where the retina does not itself provide anticipation-which is then only triggered by a cortical mechanism, the "anticipation by latency"-and unravel the effects of the retinal input amplitude, of stimulus features such as speed and contrast and of the size of cortical extensions and fiber conduction speed. Then we explore the changes in the cortical wave of anticipation when V1 is triggered by retina-driven anticipatory mechanisms: gain control and lateral inhibition by amacrine cells. Here, we show how retinal and cortical anticipation combine to provide an efficient processing where the simulated cortical response is in advance over the moving object that triggers this response, compensating the delays in visual processing.

In many behavioral conditions, neural activity propagates within and across brain regions as traveling waves, revealing the importance of analyzing spatiotemporal dynamics in electrophysiological data. Most methods quantify such propagation by measuring spatial phase gradients, i.e., monotonic and ordered phase changes through space. Here, we demonstrate that the phase ordering in travelling waves is insufficient to determine the effective flow of information unambiguously. We demonstrate that, in some specific cases, the phase gradient indicates information propagation in the opposite direction than indicated by methods for causal inference. Using autoregressive modeling, we further show that such a discrepancy between the effective waves and the apparent waves measured via phase-based methods can, for example, be predicted by the sign of the projection from the lower to the higher nodes in the hierarchy. Together with an input signal in the lowest node, inhibitory bottom-up connections produce apparent waves propagating in the opposite, top-down direction. As a methodological solution, we show that Granger causality analysis can recover the information flow and its underlying causal structure, which can be used to disambiguate the 'effective' flow. ### Competing Interest Statement The authors have declared no competing interest.

We present a method for mapping multifocal Pupillary Response Fields in a short amount of time using a visual stimulus covering 40° of the visual angle divided into nine contiguous sectors simultaneously modulated in luminance at specific, incommensurate, temporal frequencies. We test this multifocal Pupillary Frequency Tagging (mPFT) approach with young healthy participants (N = 36) and show that the spectral power of the sustained pupillary response elicited by 45 s of fixation of this multipartite stimulus reflects the relative contribution of each sector/frequency to the overall pupillary response. We further analyze the phase lag for each temporal frequency as well as several global features related to pupil state. Test/retest performed on a subset of participants indicates good repeatability. We also investigate the existence of structural (RNFL)/functional (mPFT) relationships. We then summarize the results of clinical studies conducted with mPFT on patients with neuropathies and retinopathies and show that the features derived from pupillary signal analyses, the distribution of spectral power in particular, are homologous to disease characteristics and allow for sorting patients from healthy participants with excellent sensitivity and specificity. This method thus appears as a convenient, objective, and fast tool for assessing the integrity of retino-pupillary circuits as well as idiosyncrasies and permits to objectively assess and follow-up retinopathies or neuropathies in a short amount of time.

Our daily endeavors occur in a complex visual environment, whose intrinsic variability challenges the way we integrate information to make decisions. By processing myriads of parallel sensory inputs, our brain is theoretically able to compute the variance of its environment, a cue known to guide our behavior. Yet, the neurobiological and computational basis of such variance computations are still poorly understood. Here, we quantify the dynamics of sensory variance modulations of cat primary visual cortex neurons. We report two archetypal neuronal responses, one of which is resilient to changes in variance and co-encodes the sensory feature and its variance, improving the population encoding of orientation. The existence of these variance-specific responses can be accounted for by a model of intracortical recurrent connectivity. We thus propose that local recurrent circuits process uncertainty as a generic computation, advancing our understanding of how the brain handles naturalistic inputs.

Horizontal connections in the primary visual cortex of carnivores, ungulates and primates organize on a near-regular lattice. Given the similar length scale for the regularity found in cortical orientation maps, the currently accepted theoretical standpoint is that these maps are underpinned by a like-to-like connectivity rule: horizontal axons connect preferentially to neurons with similar preferred orientation. However, there is reason to doubt the rule's explanatory power, since a growing number of quantitative studies show that the like-to-like connectivity preference and bias mostly observed at short-range scale, are highly variable on a neuron-to-neuron level and depend on the origin of the presynaptic neuron. Despite the wide availability of published data, the accepted model of visual processing has never been revised. Here, we review three lines of independent evidence supporting a much-needed revision of the like-to-like connectivity rule, ranging from anatomy to population functional measures, computational models and to theoretical approaches. We advocate an alternative, distance-dependent connectivity rule that is consistent with new structural and functional evidence: from like-to-like bias at short horizontal distance to like-to-all at long horizontal distance. This generic rule accounts for the observed high heterogeneity in interactions between the orientation and retinotopic domains, that we argue is necessary to process non-trivial stimuli in a task-dependent manner.

In our daily visual environment, the primary visual cortex (V1) processes distributions of oriented features as the basis of our visual computations. Changes of the global, median orientation of such inputs form the basis of our canonical knowledge about V1. However, another overlooked but defining characteristic of these sensory variables is their precision, which characterizes the level of variance in the input to V1. Such variability is an intrinsic part of natural images, yet it remains unclear if and how V1 accounts for the changes in orientation precision to achieve its robust orientation recognition performances. Here, we used naturalistic stimuli to characterize the response of V1 neurons to quantified variations of orientation precision. We found that about thirty percent of the recorded neurons showed a form of invariant responses to input precision. While feedforward mechanisms failed to account for the existence of these resilient neurons, neuronal competition within V1 explained the extent to which a neuron is invariant to precision. Using a decoding algorithm, we showed that the existence of such neurons in the population response of V1 can serve to encode both the orientation and its precision in the V1 population activity, which improves the robustness of the overall neural code. These precision-specific neurons operate with slow recurrent cortical dynamics, which supports the notion of predictive precisionweighted processes in V1.

Objective . Cortical activity can be recorded using a variety of tools, ranging in scale from the single neuron (microscopic) to the whole brain (macroscopic). There is usually a trade-off between scale and resolution; optical imaging techniques, with their high spatio-temporal resolution and wide field of view, are best suited to study brain activity at the mesoscale. Optical imaging of cortical areas is however in practice limited by the curvature of the brain, which causes the image quality to deteriorate significantly away from the center of the image. Approach . To address this issue and harness the full potential of optical cortical imaging techniques, we developed a new wide-field optical imaging system adapted to the macaque brain. Our system is composed of a curved detector, an aspherical lens and a ring composed of light emitting diodes providing uniform illumination at wavelengths relevant for the different optical imaging methods, including intrinsic and fluorescence imaging. Main results . The system was characterized and compared with the standard macroscope used for cortical imaging, and a three-fold increase of the area in focus was measured as well as a four-fold increase in the evenness of the optical quality in vivo . Significance . This new instrument, which is to the best of our knowledge the first use of a curved detector for cortical imaging, should facilitate the observation of wide mesoscale phenomena such as dynamic propagating waves within and between cortical maps, which are otherwise difficult to observe due to technical limitations of the currently available recording tools.

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.

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 cortical 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 model of V1 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 of complex cells as well as cortical orientation maps in V1, as observed in distinct species of mammals. By using different pooling functions, our model developed complex cells in networks that exhibit orientation maps (e.g., like in carnivores and primates) or not (e.g., rodents and lagomorphs). The SDPC can therefore be viewed as a unifying framework that explains the diversity of structural and functional phenomena observed in V1. In particular, we show that orientation maps emerge naturally as the most cost-efficient structure to generate complex cells under the predictive coding principle. Significance Cortical orientation maps are among the most fascinating structures observed in higher mammals brains: In such maps, similar orientations in the input image activate neighboring cells in the cortical surface. However, the computational advantage brought by these structures remains unclear, as some species (rodents and lagomorphs) completely lack orientation maps. In this study, we introduce a computational model that links the presence of orientation maps to a class of nonlinear neurons called complex cells. In particular, we propose that the presence or absence orientation maps correspond to different strategies employed by different species to generate invariance to complex stimuli.

Optical imaging techniques such as voltage-sensitive dye imaging and intrinsic imaging allow for the record- ing of neuronal activity at high spatio-temporal scales over a large field of view revealing some mesoscopic scale dynamics such as propagating waves. In practice however, the achievable image quality deteriorates significantly away from the point of best focus due to the curvature of the brain, which fundamentally limits the spatial extent of the cortex that can be studied through a single image. To improve the field of view achievable by optical imaging, we developed a new optical system adapted to the curvature of the non-human primate brain in study. This is achieved by using a curved detector in combination with an appropriate optical system of double Gaussian and aspherical lenses. Furthermore, to ensure a uniform and reliable illumination of the cortex, we have designed and built a new illumination system consisting of a ring of LEDs at four different wavebands. This static solution will enable imaging for the first time neuronal activity over a very large field of view (15-20mm) with high spatial and temporal resolution. Preliminary results show a significant increase of the area in focus of object imaged through the custom optics compared with the standard neuronal imaging optics.

Three rules govern the connectivity between neurons in the thalamus and inhibitory neurons in the visual cortex of rabbits.