Joint coding of shape and blur in area V4

Orthogonal neural representations support perceptual judgements of natural stimuli

In natural behavior, observers must separate relevant information from a barrage of irrelevant information. Many studies have investigated the neural underpinnings of this ability using artificial stimuli presented on simple backgrounds. Natural viewing, however, carries a set of challenges that are inaccessible using artificial stimuli, including neural responses to background objects that are task-irrelevant. An emerging body of evidence suggests that the visual abilities of humans and animals can be modeled through the linear decoding of task-relevant information from visual cortex. This idea suggests the hypothesis that irrelevant features of a natural scene should impair performance on a visual task only if their neural representations intrude on the linear readout of the task relevant feature, as would occur if the representations of task-relevant and irrelevant features are not orthogonal in the underlying neural population. We tested this hypothesis using human psychophysics and monkey neurophysiology, in response to parametrically variable naturalistic stimuli. We demonstrate that 1) the neural representation of one feature (the position of a central object) in visual area V4 is orthogonal to those of several background features, 2) the ability of human observers to precisely judge object position was largely unaffected by task-irrelevant variation in those background features, and 3) many features of the object and the background are orthogonally represented by V4 neural responses. Our observations are consistent with the hypothesis that orthogonal neural representations can support stable perception of objects and features despite the tremendous richness of natural visual scenes.

Diverse task-driven modeling of macaque V4 reveals functional specialization towards semantic tasks

Responses to natural stimuli in area V4-a mid-level area of the visual ventral stream-are well predicted by features from convolutional neural networks (CNNs) trained on image classification. This result has been taken as evidence for the functional role of V4 in object classification. However, we currently do not know if and to what extent V4 plays a role in solving other computational objectives. Here, we investigated normative accounts of V4 (and V1 for comparison) by predicting macaque single-neuron responses to natural images from the representations extracted by 23 CNNs trained on different computer vision tasks including semantic, geometric, 2D, and 3D types of tasks. We found that V4 was best predicted by semantic classification features and exhibited high task selectivity, while the choice of task was less consequential to V1 performance. Consistent with traditional characterizations of V4 function that show its high-dimensional tuning to various 2D and 3D stimulus directions, we found that diverse non-semantic tasks explained aspects of V4 function that are not captured by individual semantic tasks. Nevertheless, jointly considering the features of a pair of semantic classification tasks was sufficient to yield one of our top V4 models, solidifying V4's main functional role in semantic processing and suggesting that V4's selectivity to 2D or 3D stimulus properties found by electrophysiologists can result from semantic functional goals.

Improved modeling of human vision by incorporating robustness to blur in convolutional neural networks

Whenever a visual scene is cast onto the retina, much of it will appear degraded due to poor resolution in the periphery; moreover, optical defocus can cause blur in central vision. However, the pervasiveness of blurry or degraded input is typically overlooked in the training of convolutional neural networks (CNNs). We hypothesized that the absence of blurry training inputs may cause CNNs to rely excessively on high spatial frequency information for object recognition, thereby causing systematic deviations from biological vision. We evaluated this hypothesis by comparing standard CNNs with CNNs trained on a combination of clear and blurry images. We show that blur-trained CNNs outperform standard CNNs at predicting neural responses to objects across a variety of viewing conditions. Moreover, blur-trained CNNs acquire increased sensitivity to shape information and greater robustness to multiple forms of visual noise, leading to improved correspondence with human perception. Our results provide multi-faceted neurocomputational evidence that blurry visual experiences may be critical for conferring robustness to biological visual systems.

Indirect Electrocatalysis S─N/S─S Bond Construction by Robust Polyoxometalate Based Foams

Indirect electrocatalytic conversion of cheap organic raw materials via the activation of S─H and N─H bonds into the value-added S─N/S─S bonds chemicals for industrial rubber production is a promising strategy to realize the atomic economic reaction, during which the kinetic inhibition that is associated with the electron transfer at the electrode/electrolyte interface in traditional direct electrocatalysis can be eliminated to achieve higher performance. In this work, a series of di-copper-substituted phosphotungstatebased foams (PW10 Cu2 @CMC) are fabricated with tunable loadings (17 to 44 wt%), which can be successfully applied in indirect electrocatalytic syntheses of sulfenamides and disulfides. Specifically, the optimal PW10 Cu2 @CMC (44 wt%) exhibits excellent electrocatalytic performance for the construction of S─N/S─S bonds (yields up to 99%) coupling with the efficient production of H2 (≈50 µmol g-1  h-1 ). Remarkably, it enables the scale-up production (≈14.4 g in a batch experiment) and the obtained products can serve as rubber vulcanization accelerators with superior properties to traditional industrial rubber additives in real industrial processes. This powerful catalysis system that can simultaneously produce rubber vulcanization accelerator and H2 may inaugurate a new electrocatalytic avenue to explore polyoxometalate-based foam catalysts in electrocatalysis field.

Improved modeling of human vision by incorporating robustness to blur in convolutional neural networks

Whenever a visual scene is cast onto the retina, much of it will appear degraded due to poor resolution in the periphery; moreover, optical defocus can cause blur in central vision. However, the pervasiveness of blurry or degraded input is typically overlooked in the training of convolutional neural networks (CNNs). We hypothesized that the absence of blurry training inputs may cause CNNs to rely excessively on high spatial frequency information for object recognition, thereby causing systematic deviations from biological vision. We evaluated this hypothesis by comparing standard CNNs with CNNs trained on a combination of clear and blurry images. We show that blur-trained CNNs outperform standard CNNs at predicting neural responses to objects across a variety of viewing conditions. Moreover, blur-trained CNNs acquire increased sensitivity to shape information and greater robustness to multiple forms of visual noise, leading to improved correspondence with human perception. Our results provide novel neurocomputational evidence that blurry visual experiences are very important for conferring robustness to biological visual systems.

From intermediate shape-centered representations to the perception of oriented shapes: response to commentaries

In this response paper, we start by addressing the main points made by the commentators on the target article's main theoretical conclusions: the existence and characteristics of the intermediate shape-centered representations (ISCRs) in the visual system, their emergence from edge detection mechanisms operating on different types of visual properties, and how they are eventually reunited in higher order frames of reference underlying conscious visual perception. We also address the much-commented issue of the possible neural mechanisms of the ISCRs. In the final section, we address more specific and general comments, questions, and suggestions which, albeit very interesting, were less directly focused on the main conclusions of the target paper.

Correlation between neural responses and human perception in figure-ground segregation

Segmentation of a natural scene into objects (figures) and background (ground) is one of crucial functions for object recognition and scene understanding. Recent studies have investigated neural mechanisms underlying figure-ground (FG) segregation and reported neural modulation to FG in the intermediate-level visual area, V4, of macaque monkeys (FG neurons). However, whether FG neurons contribute to the perception of FG segregation has not been clarified. To examine the contribution of FG neurons, we examined the correlations between perceptual consistency (PC), which quantified perceptual ambiguity in FG determination, and the reliability of neural signals in response to FG. First, we evaluated PCs for the images that were used in the previous neural recording in V4; specifically, we measured how consistently FG can be determined across trials and participants for each stimulus. The PCs were widely distributed, so that we identified the ambiguity in FG segregation for each stimulus. Next, we analyzed the correlation between the PCs and the reliability of neural modulation to FG. We found that the stimuli with higher PCs evoked more consistent and greater modulation in the responses of single neurons than those with lower PCs. Since perception is expected to show a greater correlation with responses of neural population compared to those of single neurons, we examined the correlation between the PCs and the consistency of the population responses in FG determination. Stimuli with higher PCs evoked higher population consistency than those with lower PCs. Finally, we analyzed the correlation between the PCs and neural latencies in FG modulation. We found that the stimuli with higher PCs showed shorter reaction times in FG perception and evoked shorter modulation latencies in FG neurons. These results indicate that the responses of FG neurons recorded from macaque monkeys show significant correlations with human FG perception, suggesting that V4 neurons with FG-dependent responses contribute to the perception of FG segregation.

Neurons in inferior temporal cortex are sensitive to motion trajectory during degraded object recognition

Our brains continuously acquire sensory information and make judgments even when visual information is limited. In some circumstances, an ambiguous object can be recognized from how it moves, such as an animal hopping or a plane flying overhead. Yet it remains unclear how movement is processed by brain areas involved in visual object recognition. Here we investigate whether inferior temporal (IT) cortex, an area known for its relevance in visual form processing, has access to motion information during recognition. We developed a matching task that required monkeys to recognize moving shapes with variable levels of shape degradation. Neural recordings in area IT showed that, surprisingly, some IT neurons responded stronger to degraded shapes than clear ones. Furthermore, neurons exhibited motion sensitivity at different times during the presentation of the blurry target. Population decoding analyses showed that motion patterns could be decoded from IT neuron pseudo-populations. Contrary to previous findings, these results suggest that neurons in IT can integrate visual motion and shape information, particularly when shape information is degraded, in a way that has been previously overlooked. Our results highlight the importance of using challenging multifeature recognition tasks to understand the role of area IT in naturalistic visual object recognition.

Davida's deficits: weak encoding of impoverished stimuli or faulty egocentric representation?

Vannuscorps and colleagues present the fascinating case of Davida, a young person who makes systematic errors in judgments related to orientations of sharp or high-contrast visual stimuli. In this commentary, we discuss the findings in the context of observations from mid-level ventral visual stream physiology. We propose two additional interpretations for the specificity of the behavioural deficits: the observed impairments in orientation judgments may be consistent with a system that is not able to unambiguously represent certain impoverished stimuli, or with a system that is not able to translate visual input into head- or body-centered coordinates. Davida's case offers a unique glimpse into the complex cascade of transformations that enable accurate orientation judgments, and sparks curiosity about which mechanistic disruptions can produce such specific unstable percepts.

Using Blur for Perceptual Investigation and Training in Sport? A Clear Picture of the Evidence and Implications for Future Research

Dynamic, interactive sports require athletes to identify, pick-up and process relevant information in a very limited time, in order to then make an appropriate response. Perceptual-cognitive skills are, therefore, a key determinant of elite sporting performance. Recently, sport scientists have investigated ways to assess and train perceptual-cognitive skills, with one such method involving the use of blurred stimuli. Here, we describe the two main methods used to generate blur (i.e., dioptric and Gaussian) and then review the current findings in a sports context. Overall, it has been shown the use of blur can enhance performance and learning of sporting tasks in novice participants, especially when the blur is applied to peripheral stimuli. However, while intermediate and expert level participants are relatively impervious to the presence of blur, it remains to be determined if they are positive effects on learning. In a final section, we describe some of the methodological issues that limit the application of blur and then discuss the potential use of virtual reality to extend the current research base in sporting contexts.

Shape-centered representations of bounded regions of space mediate the perception of objects

We report the study of a woman who perceives 2D bounded regions of space ("shapes") defined by sharp edges of medium to high contrast as if they were rotated by 90, 180 degrees around their centre, mirrored across their own axes, or both. In contrast, her perception of 3D, strongly blurred or very low contrast shapes, and of stimuli emerging from a collection of shapes, is intact. This suggests that a stage in the process of constructing the conscious visual representation of a scene consists of representing mutually exclusive bounded regions extracted from the initial retinotopic space in "shape-centered" frames of reference. The selectivity of the disorder to shapes originally biased toward the parvocellular subcortical pathway, and the absence of any other type of error, additionally invite new hypotheses about the operations involved in computing these "intermediate shape-centered representations" and in mapping them onto higher frames for perception and action.

A cell model in the ventral visual pathway for the detection of circles of curvature constituting figures

The contour of an arbitrary figure can be represented as a group of circles of curvature in contact with it, with each curvature circle represented by its center O_C and radius r. We propose a series of cell models for detecting this circle, which is composed of a lateral geniculate nucleus (LGN) cell, nondirectionally selective (NDS) simple cell, and curvature-circle detection cell (CDC). The LGN and NDS simple cells were previously modeled. The CDC has been modeled as follows. Each tangent in contact with this circle is detected by an NDS simple cell that performs the Hough transformation of LGN cell responses, and then this tangent is transformed to a three-dimensional (3D) normal line in a CDC column. This transformation has been named a 3D normal-line transform. Performing this transformation for all tangents causes a CDC at the intersection of these normal lines to fire most intensively, and thus the O_C and r of the circle is detected as the coordinates of this intersection. Therefore, the CDC has been modeled as this 3D normal-line transform. Based on this CDC, we model two types of constancy CDC: a position-invariant CDC and a curvature-invariant CDC. These three types of CDC reflect the response to various stimuli in actual area V4 cells. In order to validate these CDC types neurophysiologically, we propose an experimental method using microelectrodes. Cell models previously reported correspond to this hierarchy: the S1, S2, and C2 cells correspond to the NDS simple cell, CDC, and position-invariant CDC, respectively.

Visual Functions of Primate Area V4

Area V4-the focus of this review-is a mid-level processing stage along the ventral visual pathway of the macaque monkey. V4 is extensively interconnected with other visual cortical areas along the ventral and dorsal visual streams, with frontal cortical areas, and with several subcortical structures. Thus, it is well poised to play a broad and integrative role in visual perception and recognition-the functional domain of the ventral pathway. Neurophysiological studies in monkeys engaged in passive fixation and behavioral tasks suggest that V4 responses are dictated by tuning in a high-dimensional stimulus space defined by form, texture, color, depth, and other attributes of visual stimuli. This high-dimensional tuning may underlie the development of object-based representations in the visual cortex that are critical for tracking, recognizing, and interacting with objects. Neurophysiological and lesion studies also suggest that V4 responses are important for guiding perceptual decisions and higher-order behavior.

Timing of response onset and offset in macaque V4: stimulus and task dependence

In the primate visual cortex, both the magnitude of the neuronal response and its timing can carry important information about the visual world, but studies typically focus only on response magnitude. Here, we examine the onset and offset latency of the responses of neurons in area V4 of awake, behaving macaques across several experiments in the context of a variety of stimuli and task paradigms. Our results highlight distinct contributions of stimuli and tasks to V4 response latency. We found that response onset latencies are shorter than typically cited (median = 75.5 ms), supporting a role for V4 neurons in rapid object and scene recognition functions. Moreover, onset latencies are longer for smaller stimuli and stimulus outlines, consistent with the hypothesis that longer latencies are associated with higher spatial frequency content. Strikingly, we found that onset latencies showed no significant dependence on stimulus occlusion, unlike in inferotemporal cortex, nor on task demands. Across the V4 population, onset latencies had a broad distribution, reflecting the diversity of feedforward, recurrent, and feedback connections that inform the responses of individual neurons. Response offset latencies, on the other hand, displayed the opposite tendency in their relationship to stimulus and task attributes: they are less influenced by stimulus appearance but are shorter in guided saccade tasks compared with fixation tasks. The observation that response latency is influenced by stimulus- and task-associated factors emphasizes a need to examine response timing alongside firing rate in determining the functional role of area V4.NEW & NOTEWORTHY Onset and offset timing of neuronal responses can provide information about visual environment and neuron's role in visual processing and its anatomical connectivity. In the first comprehensive examination of onset and offset latencies in the intermediate visual cortical area V4, we find neurons respond faster than previously reported, making them ideally suited to contribute to rapid object and scene recognition. While response onset reflects stimulus characteristics, timing of response offset is influenced more by behavioral task.

fMRI representational similarity analysis reveals graded preferences for chromatic and achromatic stimulus contrast across human visual cortex

Human visual cortex is partitioned into different functional areas that, from lower to higher, become increasingly selective and responsive to complex feature dimensions. Here we use a Representational Similarity Analysis (RSA) of fMRI-BOLD signals to make quantitative comparisons across LGN and multiple visual areas of the low-level stimulus information encoded in the patterns of voxel responses. Our stimulus set was picked to target the four functionally distinct subcortical channels that input visual cortex from the LGN: two achromatic sinewave stimuli that favor the responses of the high-temporal magnocellular and high-spatial parvocellular pathways, respectively, and two chromatic stimuli isolating the L/M-cone opponent and S-cone opponent pathways, respectively. Each stimulus type had three spatial extents to sample both foveal and para-central visual field. With the RSA, we compare quantitatively the response specializations for individual stimuli and combinations of stimuli in each area and how these change across visual cortex. First, our results replicate the known response preferences for motion/flicker in the dorsal visual areas. In addition, we identify two distinct gradients along the ventral visual stream. In the early visual areas (V1-V3), the strongest differential representation is for the achromatic high spatial frequency stimuli, suitable for form vision, and a very weak differentiation of chromatic versus achromatic contrast. Emerging in ventral occipital areas (V4, VO1 and VO2), however, is an increasingly strong separation of the responses to chromatic versus achromatic contrast and a decline in the high spatial frequency representation. These gradients provide new insight into how visual information is transformed across the visual cortex.

Object shape and surface properties are jointly encoded in mid-level ventral visual cortex

Recognizing a myriad visual objects rapidly is a hallmark of the primate visual system. Traditional theories of object recognition have focused on how crucial form features, for example, the orientation of edges, may be extracted in early visual cortex and utilized to recognize objects. An alternative view argues that much of early and mid-level visual processing focuses on encoding surface characteristics, for example, texture. Neurophysiological evidence from primate area V4 supports a third alternative - the joint, but independent, encoding of form and texture - that would be advantageous for segmenting objects from the background in natural scenes and for object recognition that is independent of surface texture. Future studies that leverage deep convolutional network models, especially focusing on network failures to match biology and behavior, can advance our insights into how such a joint representation of form and surface properties might emerge in visual cortex.

Accurate, fast, data efficient and interpretable glaucoma diagnosis with automated spatial analysis of the whole cup to disc profile

Background

Glaucoma is the leading cause of irreversible blindness worldwide. It is a heterogeneous group of conditions with a common optic neuropathy and associated loss of peripheral vision. Both over and under-diagnosis carry high costs in terms of healthcare spending and preventable blindness. The characteristic clinical feature of glaucoma is asymmetrical optic nerve rim narrowing, which is difficult for humans to quantify reliably. Strategies to improve and automate optic disc assessment are therefore needed to prevent sight loss.

Methods

We developed a novel glaucoma detection algorithm that segments and analyses colour photographs to quantify optic nerve rim consistency around the whole disc at 15-degree intervals. This provides a profile of the cup/disc ratio, in contrast to the vertical cup/disc ratio in common use. We introduce a spatial probabilistic model, to account for the optic nerve shape, we then use this model to derive a disc deformation index and a decision rule for glaucoma. We tested our algorithm on two separate image datasets (ORIGA and RIM-ONE).

Results

The spatial algorithm accurately distinguished glaucomatous and healthy discs on internal and external validation (AUROC 99.6% and 91.0% respectively). It achieves this using a dataset 100-times smaller than that required for deep learning algorithms, is flexible to the type of cup and disc segmentation (automated or semi-automated), utilises images with missing data, and is correlated with the disc size (p = 0.02) and the rim-to-disc at the narrowest rim (p<0.001, in external validation).

Discussion

The spatial probabilistic algorithm is highly accurate, highly data efficient and it extends to any imaging hardware in which the boundaries of cup and disc can be segmented, thus making the algorithm particularly applicable to research into disease mechanisms, and also glaucoma screening in low resource settings.