Interaction between central and peripheral vision: Influence of distance and spatial frequencies.

Visual scene perception is based on reciprocal interactions between central and peripheral information. Such interactions are commonly investigated through the semantic congruence effect, which usually reveals a congruence effect of central vision on peripheral vision as strong as the reverse. The aim of the present study was to further investigate the mechanisms underlying central-peripheral visual interactions using a central-peripheral congruence paradigm through three behavioral experiments. We presented simultaneously a central and a peripheral stimulus, that could be either semantically congruent or incongruent. To assess the congruence effect of central vision on peripheral vision, participants had to categorize the peripheral target stimulus while ignoring the central distractor stimulus. To assess the congruence effect of the peripheral vision on central vision, they had to categorize the central target stimulus while ignoring the peripheral distractor stimulus. Experiment 1 revealed that the physical distance between central and peripheral stimuli influences central-peripheral visual interactions: Congruence effect of central vision is stronger when the distance between the target and the distractor is the shortest. Experiments 2 and 3 revealed that the spatial frequency content of distractors also influence central-peripheral interactions: Congruence effect of central vision is observed only when the distractor contained high spatial frequencies while congruence effect of peripheral vision is observed only when the distractor contained low spatial frequencies. These results raise the question of how these influences are exerted (bottom-up vs. top-down) and are discussed based on the retinocortical properties of the visual system and the predictive brain hypothesis.

Towards an optimization of functional localizers in non-human primate neuroimaging with (fMRI) frequency-tagging.

Non-human primate (NHP) neuroimaging can provide essential insights into the neural basis of human cognitive functions. While functional magnetic resonance imaging (fMRI) localizers can play an essential role in reaching this objective (Russ et al., 2021), they often differ substantially across species in terms of paradigms, measured signals, and data analysis, biasing the comparisons. Here we introduce a functional frequency-tagging face localizer for NHP imaging, successfully developed in humans and outperforming standard face localizers (Gao et al., 2018). FMRI recordings were performed in two awake macaques. Within a rapid 6 Hz stream of natural non-face objects images, human or monkey face stimuli were presented in bursts every 9 s. We also included control conditions with phase-scrambled versions of all images. As in humans, face-selective activity was objectively identified and quantified at the peak of the face-stimulation frequency (0.111 Hz) and its second harmonic (0.222 Hz) in the Fourier domain. Focal activations with a high signal-to-noise ratio were observed in regions previously described as face-selective, mainly in the STS (clusters PL, ML, MF; also, AL, AF), both for human and monkey faces. Robust face-selective activations were also found in the prefrontal cortex of one monkey (PVL and PO clusters). Face-selective neural activity was highly reliable and excluded all contributions from low-level visual cues contained in the amplitude spectrum of the stimuli. These observations indicate that fMRI frequency-tagging provides a highly valuable approach to objectively compare human and monkey visual recognition systems within the same framework.

Efficient multi-scale representation of visual objects using a biologically plausible spike-latency code and winner-take-all inhibition.

Deep neural networks have surpassed human performance in key visual challenges such as object recognition, but require a large amount of energy, computation, and memory. In contrast, spiking neural networks (SNNs) have the potential to improve both the efficiency and biological plausibility of object recognition systems. Here we present a SNN model that uses spike-latency coding and winner-take-all inhibition (WTA-I) to efficiently represent visual stimuli using multi-scale parallel processing. Mimicking neuronal response properties in early visual cortex, images were preprocessed with three different spatial frequency (SF) channels, before they were fed to a layer of spiking neurons whose synaptic weights were updated using spike-timing-dependent-plasticity. We investigate how the quality of the represented objects changes under different SF bands and WTA-I schemes. We demonstrate that a network of 200 spiking neurons tuned to three SFs can efficiently represent objects with as little as 15 spikes per neuron. Studying how core object recognition may be implemented using biologically plausible learning rules in SNNs may not only further our understanding of the brain, but also lead to novel and efficient artificial vision systems.

Symmetry Processing in the Macaque Visual Cortex.

Symmetry is a highly salient feature of the natural world that is perceived by many species. In humans, the cerebral areas processing symmetry are now well identified from neuroimaging measurements. Macaque could constitute a good animal model to explore the underlying neural mechanisms, but a previous comparative study concluded that functional magnetic resonance imaging responses to mirror symmetry in this species were weaker than those observed in humans. Here, we re-examined symmetry processing in macaques from a broader perspective, using both rotation and reflection symmetry embedded in regular textures. Highly consistent responses to symmetry were found in a large network of areas (notably in areas V3 and V4), in line with what was reported in humans under identical experimental conditions. Our results suggest that the cortical networks that process symmetry in humans and macaques are potentially more similar than previously reported and point toward macaque as a relevant model for understanding symmetry processing.

Stereomotion Processing in the Nonhuman Primate Brain.

The cortical areas that process disparity-defined motion-in-depth (i.e., cyclopean stereomotion [CSM]) were characterized with functional magnetic resonance imaging (fMRI) in two awake, behaving macaques. The experimental protocol was similar to previous human neuroimaging studies. We contrasted the responses to dynamic random-dot patterns that continuously changed their binocular disparity over time with those to a control condition that shared the same properties, except that the temporal frames were shuffled. A whole-brain voxel-wise analysis revealed that in all four cortical hemispheres, three areas showed consistent sensitivity to CSM. Two of them were localized respectively in the lower bank of the superior temporal sulcus (CSMSTS) and on the neighboring infero-temporal gyrus (CSMITG). The third area was situated in the posterior parietal cortex (CSMPPC). Additional regions of interest-based analyses within retinotopic areas defined in both animals indicated weaker but significant responses to CSM within the MT cluster (most notably in areas MSTv and FST). Altogether, our results are in agreement with previous findings in both human and macaque and suggest that the cortical areas that process CSM are relatively well preserved between the two primate species.

Emergence of Binocular Disparity Selectivity through Hebbian Learning.

Neural selectivity in the early visual cortex strongly reflects the statistics of our environment (Barlow, 2001; Geisler, 2008). Although this has been described extensively in literature through various encoding hypotheses (Barlow and Földiák, 1989; Atick and Redlich, 1992; Olshausen and Field, 1996), an explanation as to how the cortex might develop the computational architecture to support these encoding schemes remains elusive. Here, using the more realistic example of binocular vision as opposed to monocular luminance-field images, we show how a simple Hebbian coincidence-detector is capable of accounting for the emergence of binocular, disparity selective, receptive fields. We propose a model based on spike timing-dependent plasticity, which not only converges to realistic single-cell and population characteristics, but also demonstrates how known biases in natural statistics may influence population encoding and downstream correlates of behavior. Furthermore, we show that the receptive fields we obtain are closer in structure to electrophysiological data reported in macaques than those predicted by normative encoding schemes (Ringach, 2002). We also demonstrate the robustness of our model to the input dataset, noise at various processing stages, and internal parameter variation. Together, our modeling results suggest that Hebbian coincidence detection is an important computational principle and could provide a biologically plausible mechanism for the emergence of selectivity to natural statistics in the early sensory cortex.SIGNIFICANCE STATEMENT Neural selectivity in the early visual cortex is often explained through encoding schemes that postulate that the computational aim of early sensory processing is to use the least possible resources (neurons, energy) to code the most informative features of the stimulus (information efficiency). In this article, using stereo images of natural scenes, we demonstrate how a simple Hebbian rule can lead to the emergence of a disparity-selective neural population that not only shows realistic single-cell and population tunings, but also demonstrates how known biases in natural statistics may influence population encoding and downstream correlates of behavior. Our approach allows us to view early neural selectivity, not as an optimization problem, but as an emergent property driven by biological rules of plasticity.

Asymmetry of pictorial space: A cultural phenomenon.

Art experts have argued that the mirror reversal of pictorial artworks produces an alteration of their spatial content. However, this putative asymmetry of the pictorial space remains to be empirically proved and causally explained. Here, we address these issues with the "corridor illusion," a size illusion triggered by the pictorial space of a receding corridor. We show that mirror-reversed corridors-receding respectively leftward and rightward-induce markedly different illusion strengths and thus convey distinct pictorial spaces. Remarkably, the illusion is stronger with the rightward corridor among native left-to-right readers (French participants, n = 40 males) but conversely stronger with the leftward corridor among native right-to-left readers (Syrian participants, n = 40 males). Together, these results demonstrate an asymmetry of the pictorial space and point to our reading/writing habits as a major cause of this phenomenon.

Dynamics of perceptual decisions about symmetry in visual cortex.

Neuroimaging studies have identified multiple extra-striate visual areas that are sensitive to symmetry in planar images (Kohler et al., 2016; Sasaki et al., 2005). Here, we investigated which of these areas are directly involved in perceptual decisions about symmetry, by recording high-density EEG in participants (n = 25) who made rapid judgments about whether an exemplar image contained rotation symmetry or not. Stimulus-locked sensor-level analysis revealed symmetry-specific activity that increased with increasing order of rotation symmetry. Response-locked analysis identified activity occurring between 600 and 200 ms before the button-press, that was directly related to perceptual decision making. We then used fMRI-informed EEG source imaging to characterize the dynamics of symmetry-specific activity within an extended network of areas in visual cortex. The most consistent cortical source of the stimulus-locked activity was VO1, a topographically organized area in ventral visual cortex, that was highly sensitive to symmetry in a previous study (Kohler et al., 2016). Importantly, VO1 activity also contained a strong decision-related component, suggesting that this area plays a crucial role in perceptual decisions about symmetry. Other candidate areas, such as lateral occipital cortex, had weak stimulus-locked symmetry responses and no evidence of correlation with response timing.

Processing of Egomotion-Consistent Optic Flow in the Rhesus Macaque Cortex.

The cortical network that processes visual cues to self-motion was characterized with functional magnetic resonance imaging in 3 awake behaving macaques. The experimental protocol was similar to previous human studies in which the responses to a single large optic flow patch were contrasted with responses to an array of 9 similar flow patches. This distinguishes cortical regions where neurons respond to flow in their receptive fields regardless of surrounding motion from those that are sensitive to whether the overall image arises from self-motion. In all 3 animals, significant selectivity for egomotion-consistent flow was found in several areas previously associated with optic flow processing, and notably dorsal middle superior temporal area, ventral intra-parietal area, and VPS. It was also seen in areas 7a (Opt), STPm, FEFsem, FEFsac and in a region of the cingulate sulcus that may be homologous with human area CSv. Selectivity for egomotion-compatible flow was never total but was particularly strong in VPS and putative macaque CSv. Direct comparison of results with the equivalent human studies reveals several commonalities but also some differences.

The steady-state visual evoked potential in vision research: A review.

Periodic visual stimulation and analysis of the resulting steady-state visual evoked potentials were first introduced over 80 years ago as a means to study visual sensation and perception. From the first single-channel recording of responses to modulated light to the present use of sophisticated digital displays composed of complex visual stimuli and high-density recording arrays, steady-state methods have been applied in a broad range of scientific and applied settings.The purpose of this article is to describe the fundamental stimulation paradigms for steady-state visual evoked potentials and to illustrate these principles through research findings across a range of applications in vision science.

The evolution of a disparity decision in human visual cortex.

We used fMRI-informed EEG source-imaging in humans to characterize the dynamics of cortical responses during a disparity-discrimination task. After the onset of a disparity-defined target, decision-related activity was found within an extended cortical network that included several occipital regions of interest (ROIs): V4, V3A, hMT+ and the Lateral Occipital Complex (LOC). By using a response-locked analysis, we were able to determine the timing relationships in this network of ROIs relative to the subject's behavioral response. Choice-related activity appeared first in the V4 ROI almost 200 ms before the button press and then subsequently in the V3A ROI. Modeling of the responses in the V4 ROI suggests that this area provides an early contribution to disparity discrimination. Choice-related responses were also found after the button-press in ROIs V4, V3A, LOC and hMT+. Outside the visual cortex, choice-related activity was found in the frontal and temporal poles before the button-press. By combining the spatial resolution of fMRI-informed EEG source imaging with the ability to sort out neural activity occurring before, during and after the behavioral manifestation of the decision, our study is the first to assign distinct functional roles to the extra-striate ROIs involved in perceptual decisions based on disparity, the primary cue for depth.

Dynamics and cortical distribution of neural responses to 2D and 3D motion in human.

The perception of motion-in-depth is important for avoiding collisions and for the control of vergence eye-movements and other motor actions. Previous psychophysical studies have suggested that sensitivity to motion-in-depth has a lower temporal processing limit than the perception of lateral motion. The present study used functional MRI-informed EEG source-imaging to study the spatiotemporal properties of the responses to lateral motion and motion-in-depth in human visual cortex. Lateral motion and motion-in-depth displays comprised stimuli whose only difference was interocular phase: monocular oscillatory motion was either in-phase in the two eyes (lateral motion) or in antiphase (motion-in-depth). Spectral analysis was used to break the steady-state visually evoked potentials responses down into even and odd harmonic components within five functionally defined regions of interest: V1, V4, lateral occipital complex, V3A, and hMT+. We also characterized the responses within two anatomically defined regions: the inferior and superior parietal cortex. Even harmonic components dominated the evoked responses and were a factor of approximately two larger for lateral motion than motion-in-depth. These responses were slower for motion-in-depth and were largely independent of absolute disparity. In each of our regions of interest, responses at odd-harmonics were relatively small, but were larger for motion-in-depth than lateral motion, especially in parietal cortex, and depended on absolute disparity. Taken together, our results suggest a plausible neural basis for reduced psychophysical sensitivity to rapid motion-in-depth.

tRNS boosts visual perceptual learning in participants with bilateral macular degeneration.

Perceptual learning (PL) has shown promise in enhancing residual visual functions in patients with age-related macular degeneration (MD), however it requires prolonged training and evidence of generalization to untrained visual functions is limited. Recent studies suggest that combining transcranial random noise stimulation (tRNS) with perceptual learning produces faster and larger visual improvements in participants with normal vision. Thus, this approach might hold the key to improve PL effects in MD. To test this, we trained two groups of MD participants on a contrast detection task with (n = 5) or without (n = 7) concomitant occipital tRNS. The training consisted of a lateral masking paradigm in which the participant had to detect a central low contrast Gabor target. Transfer tasks, including contrast sensitivity, near and far visual acuity, and visual crowding, were measured at pre-, mid and post-tests. Combining tRNS and perceptual learning led to greater improvements in the trained task, evidenced by a larger increment in contrast sensitivity and reduced inhibition at the shortest target to flankers' distance. The overall amount of transfer was similar between the two groups. These results suggest that coupling tRNS and perceptual learning has promising potential applications as a clinical rehabilitation strategy to improve vision in MD patients.

Disparity-specific spatial interactions: evidence from EEG source imaging.

Using cortical source estimation techniques based on high-density EEG and fMRI measurements in humans, we measured how a disparity-defined surround influenced the responses to the changing disparity of a central disk within five visual ROIs: V1, V4, lateral occipital complex (LOC), hMT+, and V3A. The responses in the V1 ROI were not consistently affected either by changes in the characteristics of the surround (correlated or uncorrelated) or by its disparity value, consistent with V1 being responsive only to absolute, not relative, disparity. Correlation in the surround increased the responses in the V4, LOC, and hMT+ ROIs over those measured with the uncorrelated surround. Thus, these extrastriate areas contain neurons that are sensitive to disparity differences. However, their evoked responses did not vary systematically with the surround disparity. Responses in the V3A ROI, in contrast, were increased by correlation in the surround and varied with its disparity. We modeled these V3A responses as attributable to a gain modulation of the absolute disparity response, where the gain amplitude is proportional to the center-surround disparity difference. An additional experiment identified a nonlinear center-surround interaction in V3A that facilitates the responses when center and surround are misaligned but suppresses it when they share the same disparity plane.

The time course of shape discrimination in the human brain.

The lateral occipital cortex (LOC) activates selectively to images of intact objects versus scrambled controls, is selective for the figure-ground relationship of a scene, and exhibits at least some degree of invariance for size and position. Because of these attributes, it is considered to be a crucial part of the object recognition pathway. Here we show that human LOC is critically involved in perceptual decisions about object shape. High-density EEG was recorded while subjects performed a threshold-level shape discrimination task on texture-defined figures segmented by either phase or orientation cues. The appearance or disappearance of a figure region from a uniform background generated robust visual evoked potentials throughout retinotopic cortex as determined by inverse modeling of the scalp voltage distribution. Contrasting responses from trials containing shape changes that were correctly detected (hits) with trials in which no change occurred (correct rejects) revealed stimulus-locked, target-selective activity in the occipital visual areas LOC and V4 preceding the subject's response. Activity that was locked to the subjects' reaction time was present in the LOC. Response-locked activity in the LOC was determined to be related to shape discrimination for several reasons: shape-selective responses were silenced when subjects viewed identical stimuli but their attention was directed away from the shapes to a demanding letter discrimination task; shape-selectivity was present across four different stimulus configurations used to define the figure; LOC responses correlated with participants' reaction times. These results indicate that decision-related activity is present in the LOC when subjects are engaged in threshold-level shape discriminations.

Optical flow estimation from event-based cameras and spiking neural networks.

Event-based cameras are raising interest within the computer vision community. These sensors operate with asynchronous pixels, emitting events, or "spikes", when the luminance change at a given pixel since the last event surpasses a certain threshold. Thanks to their inherent qualities, such as their low power consumption, low latency, and high dynamic range, they seem particularly tailored to applications with challenging temporal constraints and safety requirements. Event-based sensors are an excellent fit for Spiking Neural Networks (SNNs), since the coupling of an asynchronous sensor with neuromorphic hardware can yield real-time systems with minimal power requirements. In this work, we seek to develop one such system, using both event sensor data from the DSEC dataset and spiking neural networks to estimate optical flow for driving scenarios. We propose a U-Net-like SNN which, after supervised training, is able to make dense optical flow estimations. To do so, we encourage both minimal norm for the error vector and minimal angle between ground-truth and predicted flow, training our model with back-propagation using a surrogate gradient. In addition, the use of 3d convolutions allows us to capture the dynamic nature of the data by increasing the temporal receptive fields. Upsampling after each decoding stage ensures that each decoder's output contributes to the final estimation. Thanks to separable convolutions, we have been able to develop a light model (when compared to competitors) that can nonetheless yield reasonably accurate optical flow estimates.

Processing of translational, radial and rotational optic flow in older adults.

Aging impacts human observer's performance in a wide range of visual tasks and notably in motion discrimination. Despite numerous studies, we still poorly understand how optic flow processing is impacted in healthy older adults. Here, we estimated motion coherence thresholds in two groups of younger (age: 18-30, n = 42) and older (70-90, n = 42) adult participants for the three components of optic flow (translational, radial and rotational patterns). Stimuli were dynamic random-dot kinematograms (RDKs) projected on a large screen. Participants had to report their perceived direction of motion (leftward versus rightward for translational, inward versus outward for radial and clockwise versus anti-clockwise for rotational patterns). Stimuli had an average speed of 7°/s (additional recordings were performed at 14°/s) and were either presented full-field or in peripheral vision. Statistical analyses showed that thresholds in older adults were similar to those measured in younger participants for translational patterns, thresholds for radial patterns were significantly increased in our slowest condition and thresholds for rotational patterns were significantly decreased. Altogether, these findings support the idea that aging does not lead to a general decline in visual perception but rather has specific effects on the processing of each optic flow component.

Disparity-tuned population responses from human visual cortex.

We used source imaging of visual evoked potentials to measure neural population responses over a wide range of horizontal disparities (0.5-64 arcmin). The stimulus was a central disk that moved back and forth across the fixation plane at 2 Hz, surrounded either by binocularly uncorrelated dots (disparity noise) or by correlated dots presented in the fixation plane. Both disk and surround were composed of dynamic random dots to remove coherent monocular information. Disparity tuning was measured in five visual regions of interest (ROIs) [V1, human middle temporal area (hMT+), V4, lateral occipital complex (LOC), and V3A], defined in separate functional magnetic resonance imaging scans. The disparity tuning functions peaked between 2 and 16 arcmin for both types of surround in each ROI. Disparity tuning in the V1 ROI was unaffected by the type of surround, but surround correlation altered both the amplitude and phase of the disparity responses in the other ROIs. Response amplitude increased when the disk was in front of the surround in the V3A and LOC ROIs, indicating that these areas encode figure-ground relationships and object convexity. The correlated surround produced a consistent phase lag at the second harmonic in the hMT+ and V4 ROIs without a change in amplitude, while in the V3A ROI, both phase and amplitude effects were observed. Sensitivity to disparity context is thus widespread in visual cortex, but the dynamics of these contextual interactions differ across regions.

Bridging the gap: global disparity processing in the human visual cortex.

The human stereoscopic system is remarkable in its ability to utilize widely separated features as references to support fine depth discrimination. In a search for possible neural substrates of this ability, we recorded high-density EEG and used a distributed inverse technique to estimate population-level disparity responses in five regions of interest (ROIs): V1, V3A, hMT+, V4, and lateral occipital complex (LOC). The stimulus was a central modulating disk surrounded by a correlated "reference" annulus presented in the fixation plane. We varied a gap separating the disk from the annulus parametrically from 0 to 5.5° as a test of long-range disparity integration. In the V1, LOC, and hMT+ ROIs, the responses with gaps >0.5° were equal to those obtained in a control condition where the surround was composed of uncorrelated noise (no reference). By contrast, in the V4 and V3A ROIs, responses with gaps as large as 5.5° were still significantly higher than the control. As a test of the spatial distribution of the disparity reference information, we manipulated the properties of the stimulus by placing noise between the center and the surround or throughout the surround. The V3A ROI was particularly sensitive to disparity noise between the center and annulus regions, suggesting an important contribution of disparity edge detectors in this ROI.

Increasing the accuracy of electromagnetic inverses using functional area source correlation constraints.

Estimating cortical current distributions from electroencephalographic (EEG) or magnetoencephalographic data is a difficult inverse problem whose solution can be improved by the addition of priors on the associated neural responses. In the context of visual activation studies, we propose a new approach that uses a functional area constrained estimator (FACE) to increase the accuracy of the reconstructions. It derives the source correlation matrix from a segmentation of the cortex into areas defined by retinotopic maps of the visual field or by functional localizers obtained independently by fMRI. These areas are computed once for each individual subject and the associated estimators can therefore be reused for any new study on the same participant. The resulting FACE reconstructions emphasize the activity of sources within these areas or enforce their intercorrelations. We used realistic Monte-Carlo simulations to demonstrate that this approach improved our estimates of a diverse set of source configurations. Reconstructions obtained from a real EEG dataset demonstrate that our priors improve the localization of the cortical areas involved in horizontal disparity processing.