Form-cue invariant second-order neuronal responses to contrast modulation in primate area V2

Population coding for figure-ground texture segregation in macaque V1 and V4

Object recognition often involves the brain segregating objects from their surroundings. Neurophysiological studies of figure-ground texture segregation have yielded inconsistent results, particularly on whether V1 neurons can perform figure-ground texture segregation or just detect texture borders. To address this issue from a population perspective, we utilized two-photon calcium imaging to simultaneously record the responses of large samples of V1 and V4 neurons to figure-ground texture stimuli in awake, fixating macaques. The average response changes indicate that V1 neurons mainly detect texture borders, while V4 neurons are involved in figure-ground segregation. However, population analysis (SVM decoding of PCA-transformed neuronal responses) reveal that V1 neurons not only detect figure-ground borders, but also contribute to figure-ground texture segregation, although requiring substantially more principal components than V4 neurons to reach a 75 % decoding accuracy. Individually, V1/V4 neurons showing larger (negative/positive) figure-ground response differences contribute more to figure-ground segregation. But for V1 neurons, the contribution becomes significant only when many principal components are considered. We conclude that V1 neurons participate in figure-ground segregation primarily by defining the figure borders, and the poorly structured figure-ground information V1 neurons carry could be further utilized by V4 neurons to accomplish figure-ground segregation.

Estimating receptive fields of simple and complex cells in early visual cortex: A convolutional neural network model with parameterized rectification

Neurons in the primary visual cortex respond selectively to simple features of visual stimuli, such as orientation and spatial frequency. Simple cells, which have phase-sensitive responses, can be modeled by a single receptive field filter in a linear-nonlinear model. However, it is challenging to analyze phase-invariant complex cells, which require more elaborate models having a combination of nonlinear subunits. Estimating parameters of these models is made additionally more difficult by cortical neurons' trial-to-trial response variability. We develop a simple convolutional neural network method to estimate receptive field models for both simple and complex visual cortex cells from their responses to natural images. The model consists of a spatiotemporal filter, a parameterized rectifier unit (PReLU), and a two-dimensional Gaussian "map" of the receptive field envelope. A single model parameter determines the simple vs. complex nature of the receptive field, capturing complex cell responses as a summation of homogeneous subunits, and collapsing to a linear-nonlinear model for simple type cells. The convolutional method predicts simple and complex cell responses to natural image stimuli as well as grating tuning curves. The fitted models yield a continuum of values for the PReLU parameter across the sampled neurons, showing that the simple/complex nature of cells can vary in a continuous manner. We demonstrate that complex-like cells respond less reliably than simple-like cells. However, compensation for this unreliability with noise ceiling analysis reveals predictive performance for complex cells proportionately closer to that for simple cells. Most spatial receptive field structures are well fit by Gabor functions, whose parameters confirm well-known properties of cat A17/18 receptive fields.

Influences of local and global context on local orientation perception

Visual context modulates perception of local orientation attributes. These spatially very localised effects are considered to correspond to specific excitatory-inhibitory connectivity patterns of early visual areas as V1, creating perceptual tilt repulsion and attraction effects. Here, orientation misperception of small Gabor stimuli was used as a probe of this computational structure by sampling a large spatio-orientation space to reveal expected asymmetries due to the underlying neuronal processing. Surprisingly, the results showed a regular iso-orientation pattern of nearby location effects whose reference point was globally modulated by the spatial structure, without any complex interactions between local positions and orientation. This pattern of results was confirmed by the two perceptual parameters of bias and discrimination ability. Furthermore, the response times to stimulus configuration displayed variations that further provided evidence of how multiple early visual stages affect perception of simple stimuli.

Behavioral signatures of Y-like neuronal responses in human vision

Retinal ganglion cells initiating the magnocellular/Y-cell visual pathways respond nonlinearly to high spatial frequencies (SFs) and temporal frequencies (TFs). This nonlinearity is implicated in the processing of contrast modulation (CM) stimuli in cats and monkeys, but its contribution to human visual perception is not well understood. Here, we evaluate human psychophysical performance for CM stimuli, consisting of a high SF grating carrier whose contrast is modulated by a low SF sinewave envelope. Subjects reported the direction of motion of CM envelopes or luminance modulation (LM) gratings at different eccentricities. The performance on SF (for LMs) or carrier SF (for CMs) was measured for different TFs (LMs) or carrier TFs (CMs). The best performance for LMs was at lower TFs and SFs, decreasing systematically with eccentricity. However, performance with CMs was bandpass with carrier SF, largely independent of carrier TF, and at the highest carrier TF (20 Hz) decreased minimally with eccentricity. Since the nonlinear subunits of Y-cells respond better at higher TFs compared to the linear response components and respond best at higher SFs that are relatively independent of eccentricity, these results suggest that behavioral tasks employing CM stimuli might reveal nonlinear contributions of retinal Y-like cells to human perception.

Macaque V1 responses to 2nd-order contrast-modulated stimuli and the possible subcortical and cortical contributions

Natural images comprise contours and boundaries defined by 1st-order luminance-modulated (LM) cues that are readily encoded by V1 neurons, and 2nd-order contrast-modulated (CM) cues that carry local, but not over-the-space, luminance changes. The neurophysiological foundations for CM processing remain unsolved. Here we used two-photon calcium imaging to demonstrate that V1 superficial-layer neurons respond to both LM and CM gratings in awake, fixating, macaques, with overall LM responses stronger than CM responses. Furthermore, adaptation experiments revealed that LM responses were similarly suppressed by LM and CM adaptation, with moderately larger effects by iso-orientation adaptation than by orthogonal adaptation, suggesting that LM and CM orientation responses likely share a strong orientation-non-selective subcortical origin. In contrast, CM responses were substantially more suppressed by iso-orientation than by orthogonal LM and CM adaptation, likely suggesting stronger orientation-specific intracortical influences for CM responses than for LM responses, besides shared orientation-non-selective subcortical influences. These results thus may indicate a subcortical-to-V1 filter-rectify-filter mechanism for CM processing: Local luminance changes in CM stimuli are initially encoded by orientation-non-selective subcortical neurons, and the outputs are half-wave rectified, and then summed by V1 neurons to signal CM orientation, which may be further substantially refined by intracortical influences.

Luminance texture boundaries and luminance step boundaries are segmented using different mechanisms

In natural scenes, two adjacent surfaces may differ in mean luminance without any sharp change in luminance at their boundary, but rather due to different relative proportions of light and dark regions within each surface. We refer to such boundaries as luminance texture boundaries (LTBs), and in this study we investigate whether LTBs are segmented using different mechanisms than luminance step boundaries (LSBs). We develop a novel method to generate luminance texture boundaries from natural uniform textures, and using these natural LTB stimuli in a boundary segmentation task, we find that observers are much more sensitive to identical luminance differences which are defined by textures (LTBs) than by uniform luminance steps (LSBs), consistent with the possibility of different mechanisms. In a second and third set of experiments, we characterize observer performance segmenting natural LTBs in the presence of masking LSBs which observers are instructed to ignore. We show that there is very little effect of masking LSBs on LTB segmentation performance. Furthermore, any masking effects we find are far less than those observed in a control experiment where both the masker and target are LSBs, and far less than those predicted by a model assuming identical mechanisms. Finally, we perform a fourth set of boundary segmentation experiments using artificial LTB stimuli comprised of differing proportions of white and black dots on opposite sides of the boundary. We find that these stimuli are also highly robust to masking by supra-threshold LSBs, consistent with our results using natural stimuli, and with our earlier studies using similar stimuli. Taken as a whole, these results suggest that the visual system contains mechanisms well suited to detecting surface boundaries that are robust to interference from luminance differences arising from luminance steps like those formed by cast shadows.

Contrast-modulated stimuli produce more superimposition and predominate perception when competing with comparable luminance-modulated stimuli during interocular grouping

Interocular grouping (IOG) is a binocular visual function that can arise during multi-stable perception. IOG perception was initiated using split-grating stimuli constructed from luminance (L), luminance-modulated noise (LM) and contrast-modulated noise (CM). In Experiment 1, three different visibility levels were used for L and LM (or first-order) stimuli, and compared to fixed-visibility CM (or second-order) stimuli. Eight binocularly normal participants indicated whether they perceived full horizontal or vertical gratings, superimposition, or other (piecemeal and eye-of-origin) percepts. CM stimuli rarely generated full IOG, but predominantly generated superimposition. In Experiment 2, Levelt's modified laws were tested for IOG in nine participants. Split-gratings presented to each eye contained different visibility LM gratings, or LM and CM gratings. The results for the LM-vs-LM conditions mostly followed the predictions of Levelt's modified laws, whereas the results for the LM-vs-CM conditions did not. Counterintuitively, when high-visibility LM and low-visibility CM split-gratings were used, high-visibility LM components did not predominate IOG perception. Our findings suggest that higher proportions of superimposition during CM-vs-CM viewing are due to binocular combination, rather than mutual inhibition. It implies that IOG percepts are more likely to be mediated at an earlier monocular, rather than a binocular stage. Our previously proposed conceptual framework for conventional binocular rivalry, which includes asymmetric feedback, visual saliency, or a combination of both (Skerswetat et al. Sci Rep 8:14432, 2018), might also account for IOG. We speculate that opponency neurons might mediate coherent percepts when dissimilar information separately enters the eyes.

Integration of contours defined by second-order contrast-modulation of texture

Boundaries in the visual world can be defined by changes in luminance and texture in the input image. A "contour integration" process joins together local changes into percepts of lines or edges. A previous study tested the integration of contours defined by second-order contrast-modulation. Their contours were placed in a background of random wavelets. Participants performed near chance. We re-visited second-order contour integration with a different task. Participants distinguished contours with "good continuation" from distractors. We measured thresholds in different amounts of external orientation or position noise. This gave two noise-masking functions. We also measured thresholds for contours with a baseline curvature to assess performance with more curvy targets. Our participants were able to discriminate the good continuation of second-order contours. Thresholds were higher than for first-order contours. In our modelling, we found this was due to multiple factors. There was a doubling of equivalent internal noise between first- and second-order contour integration. There was also a reduction in efficiency. The efficiency difference was only significant in our orientation noise condition. For both first- and second-order stimuli, participants were also able to perform our task with more curved contours. We conclude that humans can integrate second-order contours, even when they are curved. There is however reduced performance compared to first-order contours. We find both an impaired input to the integrating mechanism, and reduced efficiency seem responsible. Second-order contour integration may be more affected by the noise background used in the previous study. Difficulty segregating that background may explain their result.

Modeling second-order boundary perception: A machine learning approach

Visual pattern detection and discrimination are essential first steps for scene analysis. Numerous human psychophysical studies have modeled visual pattern detection and discrimination by estimating linear templates for classifying noisy stimuli defined by spatial variations in pixel intensities. However, such methods are poorly suited to understanding sensory processing mechanisms for complex visual stimuli such as second-order boundaries defined by spatial differences in contrast or texture. We introduce a novel machine learning framework for modeling human perception of second-order visual stimuli, using image-computable hierarchical neural network models fit directly to psychophysical trial data. This framework is applied to modeling visual processing of boundaries defined by differences in the contrast of a carrier texture pattern, in two different psychophysical tasks: (1) boundary orientation identification, and (2) fine orientation discrimination. Cross-validation analysis is employed to optimize model hyper-parameters, and demonstrate that these models are able to accurately predict human performance on novel stimulus sets not used for fitting model parameters. We find that, like the ideal observer, human observers take a region-based approach to the orientation identification task, while taking an edge-based approach to the fine orientation discrimination task. How observers integrate contrast modulation across orientation channels is investigated by fitting psychophysical data with two models representing competing hypotheses, revealing a preference for a model which combines multiple orientations at the earliest possible stage. Our results suggest that this machine learning approach has much potential to advance the study of second-order visual processing, and we outline future steps towards generalizing the method to modeling visual segmentation of natural texture boundaries. This study demonstrates how machine learning methodology can be fruitfully applied to psychophysical studies of second-order visual processing.

Many naturally occurring visual boundaries are defined by spatial differences in features other than luminance, for example by differences in texture or contrast. Quantitative models of such “second-order” boundary perception cannot be estimated using the standard regression techniques (known as “classification images”) commonly applied to “first-order”, luminance-defined stimuli. Here we present a novel machine learning approach to modeling second-order boundary perception using hierarchical neural networks. In contrast to previous quantitative studies of second-order boundary perception, we directly estimate network model parameters using psychophysical trial data. We demonstrate that our method can reveal different spatial summation strategies that human observers utilize for different kinds of second-order boundary perception tasks, and can be used to compare competing hypotheses of how contrast modulation is integrated across orientation channels. We outline extensions of the methodology to other kinds of second-order boundaries, including those in natural images.

Aging affects correlation within the V1 neuronal population in rhesus monkeys

Visual function declines with age. This deterioration results not only from changes in the optical system but also from the functional degradation of the central visual cortex. Although numerous studies have explored the mechanisms of age-related influences on vision, they have failed to acknowledge the significance of neuronal correlation in dysfunction of the visual cortex. Previous research has focused on the functional degradation of individual neurons, with age-induced changes in correlation between neurons still unknown. In the present study, using electrophysiological techniques, we investigated the age-related changes in neuronal correlation in the macaque V1 area and the underlying mechanisms of those changes. Our results showed that aging led to an increase in the correlation of neurons and changed the noise-signal correlation structure, which may impact population coding efficiency. Furthermore, we found that the age-induced decline in the inhibitory circuitry accounted for the alteration in neuronal correlation.

Scale-Invariant Visual Capabilities Explained by Topographic Representations of Luminance and Texture in Primate V1

Humans have remarkable scale-invariant visual capabilities. For example, our orientation discrimination sensitivity is largely constant over more than two orders of magnitude of variations in stimulus spatial frequency (SF). Orientation-selective V1 neurons are likely to contribute to orientation discrimination. However, because at any V1 location neurons have a limited range of receptive field (RF) sizes, we predict that at low SFs V1 neurons will carry little orientation information. If this were the case, what could account for the high behavioral sensitivity at low SFs? Using optical imaging in behaving macaques, we show that, as predicted, V1 orientation-tuned responses drop rapidly with decreasing SF. However, we reveal a surprising coarse-scale signal that corresponds to the projection of the luminance layout of low-SF stimuli to V1's retinotopic map. This homeomorphic and distributed representation, which carries high-quality orientation information, is likely to contribute to our striking scale-invariant visual capabilities.

Levelt's laws do not predict perception when luminance- and contrast-modulated stimuli compete during binocular rivalry

Incompatible patterns viewed by each of the two eyes can provoke binocular rivalry, a competition of perception. Levelt's first law predicts that a highly visible stimulus will predominate over a less visible stimulus during binocular rivalry. In a behavioural study, we made a counterintuitive observation: high visibility patterns do not always predominate over low visibility patterns. Our results show that none of Levelt's binocular rivalry laws hold when luminance-modulated (LM) patterns compete with contrast-modulated (CM) patterns. We discuss visual saliency, asymmetric feedback, and a combination of both as potential mechanisms to explain the CM versus LM findings. Competing orthogonal LM stimuli do follow Levelt's laws, whereas only the first two laws hold for competing CM stimuli. The current results provide strong psychophysical evidence for the existence of separate processing stages for LM and CM stimuli.

Object size determines the spatial spread of visual time

A key question for temporal processing research is how the nervous system extracts event duration, despite a notable lack of neural structures dedicated to duration encoding. This is in stark contrast with the orderly arrangement of neurons tasked with spatial processing. In this study, we examine the linkage between the spatial and temporal domains. We use sensory adaptation techniques to generate after-effects where perceived duration is either compressed or expanded in the opposite direction to the adapting stimulus' duration. Our results indicate that these after-effects are broadly tuned, extending over an area approximately five times the size of the stimulus. This region is directly related to the size of the adapting stimulus-the larger the adapting stimulus the greater the spatial spread of the after-effect. We construct a simple model to test predictions based on overlapping adapted versus non-adapted neuronal populations and show that our effects cannot be explained by any single, fixed-scale neural filtering. Rather, our effects are best explained by a self-scaled mechanism underpinned by duration selective neurons that also pool spatial information across earlier stages of visual processing.

More superimposition for contrast-modulated than luminance-modulated stimuli during binocular rivalry

Luminance-modulated noise (LM) and contrast-modulated noise (CM) gratings were presented with interocularly correlated, uncorrelated and anti-correlated binary noise to investigate their contributions to mixed percepts, specifically piecemeal and superimposition, during binocular rivalry. Stimuli were sine-wave gratings of 2 c/deg presented within 2 deg circular apertures. The LM stimulus contrast was 0.1 and the CM stimulus modulation depth was 1.0, equating to approximately 5 and 7 times detection threshold, respectively. Twelve 45 s trials, per noise configuration, were carried out. Fifteen participants with normal vision indicated via button presses whether an exclusive, piecemeal or superimposed percept was seen. For all noise conditions LM stimuli generated more exclusive visibility, and lower proportions of superimposition. CM stimuli led to greater proportions and longer periods of superimposition. For both stimulus types, correlated interocular noise generated more superimposition than did anti- or uncorrelated interocular noise. No significant effect of stimulus type (LM vs CM) or noise configuration (correlated, uncorrelated, anti-correlated) on piecemeal perception was found. Exclusive visibility was greater in proportion, and perceptual changes more numerous, during binocular rivalry for CM stimuli when interocular noise was not correlated. This suggests that mutual inhibition, initiated by non-correlated noise CM gratings, occurs between neurons processing luminance noise (first-order component), as well as those processing gratings (second-order component). Therefore, first- and second-order components can contribute to overall binocular rivalry responses. We suggest the addition of a new well to the current energy landscape model for binocular rivalry that takes superimposition into account.

On texture, form, and fixational eye movements

Recent studies show that small movements of the eye that occur during fixation are controlled in the brain by similar neural mechanisms as large eye movements. Information theory has been successful in explaining many properties of large eye movements. Could it also help us understand the smaller eye movements that are much more difficult to study experimentally? Here I describe new predictions for how small amplitude fixational eye movements should be modulated by visual context in order to improve visual perception. In particular, the amplitude of fixational eye movements is predicted to differ when localizing edges defined by changes in texture or luminance.

Nonlinear Y-Like Receptive Fields in the Early Visual Cortex: An Intermediate Stage for Building Cue-Invariant Receptive Fields from Subcortical Y Cells

Many of the neurons in early visual cortex are selective for the orientation of boundaries defined by first-order cues (luminance) as well as second-order cues (contrast, texture). The neural circuit mechanism underlying this selectivity is still unclear, but some studies have proposed that it emerges from spatial nonlinearities of subcortical Y cells. To understand how inputs from the Y-cell pathway might be pooled to generate cue-invariant receptive fields, we recorded visual responses from single neurons in cat Area 18 using linear multielectrode arrays. We measured responses to drifting and contrast-reversing luminance gratings as well as contrast modulation gratings. We found that a large fraction of these neurons have nonoriented responses to gratings, similar to those of subcortical Y cells: they respond at the second harmonic (F2) to high-spatial frequency contrast-reversing gratings and at the first harmonic (F1) to low-spatial frequency drifting gratings ("Y-cell signature"). For a given neuron, spatial frequency tuning for linear (F1) and nonlinear (F2) responses is quite distinct, similar to orientation-selective cue-invariant neurons. Also, these neurons respond to contrast modulation gratings with selectivity for the carrier (texture) spatial frequency and, in some cases, orientation. Their receptive field properties suggest that they could serve as building blocks for orientation-selective cue-invariant neurons. We propose a circuit model that combines ON- and OFF-center cortical Y-like cells in an unbalanced push-pull manner to generate orientation-selective, cue-invariant receptive fields.

SIGNIFICANCE STATEMENT

A significant fraction of neurons in early visual cortex have specialized receptive fields that allow them to selectively respond to the orientation of boundaries that are invariant to the cue (luminance, contrast, texture, motion) that defines them. However, the neural mechanism to construct such versatile receptive fields remains unclear. Using multielectrode recording, we found a large fraction of neurons in early visual cortex with receptive fields not selective for orientation that have spatial nonlinearities like those of subcortical Y cells. These are strong candidates for building cue-invariant orientation-selective neurons; we present a neural circuit model that pools such neurons in an imbalanced "push-pull" manner, to generate orientation-selective cue-invariant receptive fields.

Phase-Dependent Interactions in Visual Cortex to Combinations of First- and Second-Order Stimuli

A fundamental task of the visual system is to extract figure-ground boundaries between objects, which are often defined, not only by differences in luminance, but also by "second-order" contrast or texture differences. Responses of cortical neurons to both first- and second-order patterns have been studied extensively, but only for responses to either type of stimulus in isolation. Here, we examined responses of visual cortex neurons to the spatial relationship between superimposed periodic luminance modulation (LM) and contrast modulation (CM) stimuli, the contrasts of which were adjusted to give equated responses when presented alone. Extracellular single-unit recordings were made in area 18 of the cat, the neurons of which show responses to CM and LM stimuli very similar to those in primate area V2 (Li et al., 2014). Most neurons showed a significant dependence on the relative phase of the combined LM and CM patterns, with a clear overall optimal response when they were approximately phase aligned. The degree of this phase preference, and the contributions of suppressive and/or facilitatory interactions, varied considerably from one neuron to another. Such phase-dependent and phase-invariant responses were evident in both simple- and complex-type cells. These results place important constraints on any future model of the underlying neural circuitry for second-order responses. The diversity in the degree of phase dependence between LM and CM stimuli that we observed could help to disambiguate different kinds of boundaries in natural scenes.

SIGNIFICANCE STATEMENT

Many visual cortex neurons exhibit orientation-selective responses to boundaries defined by differences either in luminance or in texture contrast. Previous studies have examined responses to either type of boundary in isolation, but here we measured systematically responses of cortical neurons to the spatial relationship between superimposed periodic luminance-modulated (LM) and contrast-modulated (CM) stimuli with contrasts adjusted to give equated responses. We demonstrate that neuronal responses to these compound stimuli are highly dependent on the relative phase between the LM and CM components. Diversity in the degree of such phase dependence could help to disambiguate different kinds of boundaries in natural scenes, for example, those arising from surface reflectance changes or from illumination gradients such as shading or shadows.

Cross-orientation suppression in visual area V2

Object recognition relies on a series of transformations among which only the first cortical stage is relatively well understood. Already at the second stage, the visual area V2, the complexity of the transformation precludes a clear understanding of what specifically this area computes. Previous work has found multiple types of V2 neurons, with neurons of each type selective for multi-edge features. Here we analyse responses of V2 neurons to natural stimuli and find three organizing principles. First, the relevant edges for V2 neurons can be grouped into quadrature pairs, indicating invariance to local translation. Second, the excitatory edges have nearby suppressive edges with orthogonal orientations. Third, the resulting multi-edge patterns are repeated in space to form textures or texture boundaries. The cross-orientation suppression increases the sparseness of responses to natural images based on these complex forms of feature selectivity while allowing for multiple scales of position invariance.

V2 neurons exhibit complex and diverse selectivity for visual features. Here the authors use a statistical analytical framework to model V2 responses to natural stimuli and find three organizing principles, chief among them is the cross-orientation suppression that increases response selectivity.

Altered interhemispheric functional connectivity in patients with anisometropic and strabismic amblyopia: a resting-state fMRI study

PURPOSE

Altered brain functional connectivity has been reported in patients with amblyopia by recent neuroimaging studies. However, relatively little is known about the alterations in interhemispheric functional connectivity in amblyopia. The present study aimed to investigate the functional connectivity patterns between homotopic regions across hemispheres in patients with anisometropic and strabismic amblyopia under resting state.

METHODS

Nineteen monocular anisometropic amblyopia (AA), 18 strabismic amblyopia (SA), and 20 normal-sight controls (NC) were enrolled in this study. After a comprehensive ophthalmologic examination, resting-state fMRI scanning was performed in all participants. The pattern of the interhemispheric functional connectivity was measured with the voxel-mirrored homotopic connectivity (VMHC) approach. VMHC values differences within and between three groups were compared, and correlations between VMHC values and each the clinical variable were also analyzed.

RESULTS

Altered VMHC was observed in AA and SA patients in lingual gyrus and fusiform gyrus compared with NC subjects. The altered VMHC of lingual gyrus showed a pattern of AA > SA > NC, while the altered VMHC of fusiform gyrus showed a pattern of AA > NC > SA. Moreover, the VMHC values of lingual gyrus were positively correlated with the stereoacuity both in AA and SA patients, and the VMHC values of fusiform gyrus were positively correlated with the amount of anisometropia just in AA patients.

CONCLUSION

These findings suggest that interhemispheric functional coordination between several homotopic visual-related brain regions is impaired both in AA and SA patients under resting state and revealed the similarities and differences in interhemispheric functional connectivity between the anisometropic and strabismic amblyopia.

Neural Processing of Second-Order Motion in the Suprasylvian Cortex of the Cat

Neuronal responses to second-order motion, that is, to spatiotemporal variations of texture or contrast, have been reported in several cortical areas of mammals, including the middle-temporal (MT) area in primates. In this study, we investigated whether second-order responses are present in the cat posteromedial lateral suprasylvian (PMLS) cortex, a possible homolog of the primate area MT. The stimuli used were luminance-based sine-wave gratings (first-order) and contrast-modulated carrier stimuli (second-order), which consisted of a high-spatial-frequency static grating (carrier) whose contrast was modulated by a low-spatial-frequency drifting grating (envelope). Results indicate that most PMLS neurons responded to second-order motion and for the vast majority of cells, first- and second-order preferred directions were conserved. However, responses to second-order stimuli were significantly reduced when compared to those evoked by first-order gratings. Circular variance was increased for second-order stimuli, indicating that PMLS direction selectivity was weaker for this type of stimulus. Finally, carrier orientation selectivity was either absent or very broad and had no influence on the envelope's orientation selectivity. In conclusion, our data show that PMLS neurons exhibit similar first- and second-order response profiles and that, akin primate area MT cells, they perform a form-cue invariant analysis of motion signals.

Binocular functional architecture for detection of contrast-modulated gratings

Combination of signals from the two eyes is the gateway to stereo vision. To gain insight into binocular signal processing, we studied binocular summation for luminance-modulated gratings (L or LM) and contrast-modulated gratings (CM). We measured 2AFC detection thresholds for a signal grating (0.75c/deg, 216ms) shown to one eye, both eyes, or both eyes out-of-phase. For LM and CM, the carrier noise was in both eyes, even when the signal was monocular. Mean binocular thresholds for luminance gratings (L) were 5.4dB better than monocular thresholds - close to perfect linear summation (6dB). For LM and CM the binocular advantage was again 5-6dB, even when the carrier noise was uncorrelated, anti-correlated, or at orthogonal orientations in the two eyes. Binocular combination for CM probably arises from summation of envelope responses, and not from summation of these conflicting carrier patterns. Antiphase signals produced no binocular advantage, but thresholds were about 1-3dB higher than monocular ones. This is not consistent with simple linear summation, which should give complete cancellation and unmeasurably high thresholds. We propose a three-channel model in which noisy monocular responses to the envelope are binocularly combined in a contrast-weighted sum, but also remain separately available to perception via a max operator. Vision selects the largest of the three responses. With in-phase gratings the binocular channel dominates, but antiphase gratings cancel in the binocular channel and the monocular channels mediate detection. The small antiphase disadvantage might be explained by a subtle influence of background responses on binocular and monocular detection.

Mice Can Use Second-Order, Contrast-Modulated Stimuli to Guide Visual Perception

Visual processing along the primate ventral stream takes place in a hierarchy of areas, characterized by an increase in both complexity of neuronal preferences and invariance to changes of low-level stimulus attributes. A basic type of invariance is form-cue invariance, where neurons have similar preferences in response to first-order stimuli, defined by changes in luminance, and global features of second-order stimuli, defined by changes in texture or contrast. Whether in mice, a now popular model system for early visual processing, visual perception can be guided by second-order stimuli is currently unknown. Here, we probed mouse visual perception and neural responses in areas V1 and LM using various types of second-order, contrast-modulated gratings with static noise carriers. These gratings differ in their spatial frequency composition and thus in their ability to invoke first-order mechanisms exploiting local luminance features. We show that mice can transfer learning of a coarse orientation discrimination task involving first-order, luminance-modulated gratings to the contrast-modulated gratings, albeit with markedly reduced discrimination performance. Consistent with these behavioral results, we demonstrate that neurons in area V1 and LM are less responsive and less selective to contrast-modulated than to luminance-modulated gratings, but respond with broadly similar preferred orientations. We conclude that mice can, at least in a rudimentary form, use second-order stimuli to guide visual perception.

SIGNIFICANCE STATEMENT

To extract object boundaries in natural scenes, the primate visual system does not only rely on differences in local luminance but can also take into account differences in texture or contrast. Whether the mouse, which has a much simpler visual system, can use such second-order information to guide visual perception is unknown. Here we tested mouse perception of second-order, contrast-defined stimuli and measured their neural representations in two areas of visual cortex. We find that mice can use contrast-defined stimuli to guide visual perception, although behavioral performance and neural representations were less robust than for luminance-defined stimuli. These findings shed light on basic steps of feature extraction along the mouse visual cortical hierarchy, which may ultimately lead to object recognition.

Encoding and estimation of first- and second-order binocular disparity in natural images

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First- and second-order responses to natural binocular images are correlated.

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Second-order mechanisms can improve the accuracy of disparity estimation.

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Second-order mechanisms can extend the depth range of binocular stereopsis.

The first stage of processing of binocular information in the visual cortex is performed by mechanisms that are bandpass-tuned for spatial frequency and orientation. Psychophysical and physiological evidence have also demonstrated the existence of second-order mechanisms in binocular processing, which can encode disparities that are not directly accessible to first-order mechanisms. We compared the responses of first- and second-order binocular filters to natural images. We found that the responses of the second-order mechanisms are to some extent correlated with the responses of the first-order mechanisms, and that they can contribute to increasing both the accuracy, and depth range, of binocular stereopsis.

A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2

Previous theoretical and experimental studies have demonstrated tight relationships between natural image statistics and neural representations in V1. In particular, receptive field properties similar to simple and complex cells have been shown to be inferable from sparse coding of natural images. However, whether such a relationship exists in higher areas has not been clarified. To address this question for V2, we trained a sparse coding model that took as input the output of a fixed V1-like model, which was in its turn fed a large variety of natural image patches as input. After the training, the model exhibited response properties that were qualitatively and quantitatively compatible with three major neurophysiological results on macaque V2, as follows: (1) homogeneous and heterogeneous integration of local orientations (Anzai et al., 2007); (2) a wide range of angle selectivities with biased sensitivities to one component orientation (Ito and Komatsu, 2004); and (3) exclusive length and width suppression (Schmid et al., 2014). The reproducibility was stable across variations in several model parameters. Further, a formal classification of the internal representations of the model units offered detailed interpretations of the experimental data, emphasizing that a novel type of model cell that could detect a combination of local orientations converging toward a single spatial point (potentially related to corner-like features) played an important role in reproducing tuning properties compatible with V2. These results are consistent with the idea that V2 uses a sparse code of natural images. Significance statement: Sparse coding theory has successfully explained a number of receptive field properties in V1; but how about in V2? This question has recently become important since a variety of properties distinct from V1 have been discovered in V2, and thus a more integrative understanding is called for. Our study shows that a hierarchical sparse coding model of natural images explains three major response properties known in the macaque V2. We further provide a detailed analysis revealing the roles of different kinds of model cells in explaining the V2-specific properties. Our results thus offer the first sparse coding account for receptive field properties in V2 that has extensive biological relevance.

A conceptual framework of computations in mid-level vision

If a picture is worth a thousand words, as an English idiom goes, what should those words-or, rather, descriptors-capture? What format of image representation would be sufficiently rich if we were to reconstruct the essence of images from their descriptors? In this paper, we set out to develop a conceptual framework that would be: (i) biologically plausible in order to provide a better mechanistic understanding of our visual system; (ii) sufficiently robust to apply in practice on realistic images; and (iii) able to tap into underlying structure of our visual world. We bring forward three key ideas. First, we argue that surface-based representations are constructed based on feature inference from the input in the intermediate processing layers of the visual system. Such representations are computed in a largely pre-semantic (prior to categorization) and pre-attentive manner using multiple cues (orientation, color, polarity, variation in orientation, and so on), and explicitly retain configural relations between features. The constructed surfaces may be partially overlapping to compensate for occlusions and are ordered in depth (figure-ground organization). Second, we propose that such intermediate representations could be formed by a hierarchical computation of similarity between features in local image patches and pooling of highly-similar units, and reestimated via recurrent loops according to the task demands. Finally, we suggest to use datasets composed of realistically rendered artificial objects and surfaces in order to better understand a model's behavior and its limitations.

Contextual modulation as de-texturizer

Contextual modulation refers to the effect of texture placed outside of a neuron's classical receptive field as well as the effect of surround texture on the perceptual properties of variegated regions within. In this minireview, we argue that one role of contextual modulation is to enhance the perception of contours at the expense of textures, in short to de-texturize the image. The evidence for this role comes mainly from three sources: psychophysical studies of shape after-effects, computational models of neurons that exhibit iso-orientation surround inhibition, and fMRI studies revealing specialized areas for contour as opposed to texture processing. The relationship between psychophysical studies that support the notion of contextual modulation as de-texturizer and those that investigate contour integration and crowding is discussed.