Model-Based Approach Shows ON Pathway Afferents Elicit a Transient Decrease of V1 Responses.

Neurons in the primary visual cortex (V1) receive excitation and inhibition from distinct parallel pathways processing lightness (ON) and darkness (OFF). V1 neurons overall respond more strongly to dark than light stimuli, consistent with a preponderance of darker regions in natural images, as well as human psychophysics. However, it has been unclear whether this "dark-dominance" is because of more excitation from the OFF pathway or more inhibition from the ON pathway. To understand the mechanisms behind dark-dominance, we record electrophysiological responses of individual simple-type V1 neurons to natural image stimuli and then train biologically inspired convolutional neural networks to predict the neurons' responses. Analyzing a sample of 71 neurons (in anesthetized, paralyzed cats of either sex) has revealed their responses to be more driven by dark than light stimuli, consistent with previous investigations. We show that this asymmetry is predominantly because of slower inhibition to dark stimuli rather than to stronger excitation from the thalamocortical OFF pathway. Consistent with dark-dominant neurons having faster responses than light-dominant neurons, we find dark-dominance to solely occur in the early latencies of neurons' responses. Neurons that are strongly dark-dominated also tend to be less orientation-selective. This novel approach gives us new insight into the dark-dominance phenomenon and provides an avenue to address new questions about excitatory and inhibitory integration in cortical neurons.SIGNIFICANCE STATEMENT Neurons in the early visual cortex respond on average more strongly to dark than to light stimuli, but the mechanisms behind this bias have been unclear. Here we address this issue by combining single-unit electrophysiology with a novel machine learning model to analyze neurons' responses to natural image stimuli in primary visual cortex. Using these techniques, we find slower inhibition to light than to dark stimuli to be the leading mechanism behind stronger dark responses. This slower inhibition to light might help explain other empirical findings, such as why orientation selectivity is weaker at earlier response latencies. These results demonstrate how imbalances in excitation versus inhibition can give rise to response asymmetries in cortical neuron responses.

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.

Visual perception of texture regularity: Conjoint measurements and a wavelet response-distribution model.

Texture regularity, such as the repeating pattern in a carpet, brickwork or tree bark, is a ubiquitous feature of the visual world. The perception of regularity has generally been studied using multi-element textures in which the degree of regularity has been manipulated by adding random jitter to the elements' positions. Here we used three-factor Maximum Likelihood Conjoint Measurement (MLCM) for the first time to investigate the encoding of regularity information under more complex conditions in which element spacing and size, in addition to positional jitter, were manipulated. Human observers were presented with large numbers of pairs of multi-element stimuli with varying levels of the three factors, and indicated on each trial which stimulus appeared more regular. All three factors contributed to regularity perception. Jitter, as expected, strongly affected regularity perception. This effect of jitter on regularity perception is strongest at small element spacing and large texture element size, suggesting that the visual system utilizes the edge-to-edge distance between elements as the basis for regularity judgments. We then examined how the responses of a bank of Gabor wavelet spatial filters might account for our results. Our analysis indicates that the peakedness of the spatial frequency (SF) distribution, a previously favored proposal, is insufficient for regularity encoding since it varied more with element spacing and size than with jitter. Instead, our results support the idea that the visual system may extract texture regularity information from the moments of the SF-distribution across orientation. In our best-performing model, the variance of SF-distribution skew across orientations can explain 70% of the variance of estimated texture regularity from our data, suggesting that it could provide a candidate read-out for perceived regularity.

Segmenting surface boundaries using luminance cues.

Segmenting scenes into distinct surfaces is a basic visual perception task, and luminance differences between adjacent surfaces often provide an important segmentation cue. However, mean luminance differences between two surfaces may exist without any sharp change in albedo at their boundary, but rather from differences in the proportion of small light and dark areas within each surface, e.g. texture elements, which we refer to as a luminance texture boundary. Here we investigate the performance of human observers segmenting luminance texture boundaries. We demonstrate that a simple model involving a single stage of filtering cannot explain observer performance, unless it incorporates contrast normalization. Performing additional experiments in which observers segment luminance texture boundaries while ignoring super-imposed luminance step boundaries, we demonstrate that the one-stage model, even with contrast normalization, cannot explain performance. We then present a Filter-Rectify-Filter model positing two cascaded stages of filtering, which fits our data well, and explains observers' ability to segment luminance texture boundary stimuli in the presence of interfering luminance step boundaries. We propose that such computations may be useful for boundary segmentation in natural scenes, where shadows often give rise to luminance step edges which do not correspond to surface boundaries.

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.

Perceived regularity of a texture is influenced by the regularity of a surrounding texture.

Previous studies have shown that texture regularity is adaptable, and have suggested that texture regularity might be coded by the peakedness of the underlying spatial frequency distribution. Here we demonstrate the related phenomenon of simultaneous regularity contrast (SRC), in which the perceived regularity of a central texture is influenced by the regularity of a surrounding texture. We presented center-surround arrangements of textures and measured the perceived regularity of the centre, using a centre-only comparison stimulus and a 2AFC procedure. From the resulting psychometric functions the SRC was measured as the difference between test and comparison regularity at the PSE (point of subjective equality). Observers generally exhibited asymmetric bidirectional SRC, in that more regular surrounds decreased the perceived regularity of the centre by between 20-40%, while less regular surrounds increased the perceived regularity of the centre by about 10%. Consistent with previous studies, a wavelet spatial frequency (SF) analysis of the stimuli revealed that their SF distributions became sharper with increased regularity, and therefore that distribution statistics such as kurtosis and SF bandwidth might be used to code regularity.

Simultaneous density contrast and binocular integration.

Most research on texture density has utilized textures rendered as two-dimensional (2D) planar surfaces, consistent with the conventional definition of density as the number of texture elements per unit area. How the brain represents texture density information in the three-dimensional (3D) world is not yet clear. Here we tested whether binocular information affects density processing using simultaneous density contrast (SDC), in which the perceived density of a texture region is changed by a surround of different density. We considered the effect on SDC of two types of binocular information: the stereoscopic depth relationships and the interocular relationships between the center and surround textures. Observers compared the perceived density of two random dot patterns, one with a surround (test stimulus) and one without (match), using a 2AFC staircase procedure. In Experiment 1 we manipulated the stereo-depth of the surround plane systematically from near to far, relative to the center plane. SDC was reduced when the difference in stereo-depth between test center and surround increased. In Experiment 2 we spread the surround dots randomly across a stereo-depth volume from small to large volume sizes, and found that SDC was slightly reduced with volume size. The decrease of SDC in both experiments was observed with dense surrounds only, but not with sparse surrounds. In the last experiment we presented center and surround in the same depth plane but dichopticly, monopticly, and binocularly. A strong interocular transfer of SDC was found in the dichoptic condition. Together these results show that texture density processing is sensitive to binocularity.

Texture density adaptation can be bidirectional.

Texture density has previously been thought of as a scalar attribute on the assumption that texture density adaptation only reduces, not enhances, perceived density (Durgin & Huk, 1997). This "unidirectional" property of density adaptation is in contradistinction to the finding that simultaneous density contrast (SDC) is "bidirectional"; that is, not only do denser surrounds reduce the perceived density of a lower density region, but sparser surrounds enhance it (Sun, Baker, & Kingdom, 2016). Here we reexamine the directionality of density adaptation using random dot patterns and a two-alternative forced choice task in which observers compare the perceived density of adapted test patches with unadapted match stimuli. In the first experiment, we observed a unidirectional density aftereffect when test and match were presented simultaneously as in previous studies. However, when they were presented sequentially, bidirectionality was obtained. This bidirectional aftereffect remained when the presentation order of test and match was reversed (second experiment). In the third experiment, we used sequential presentation to measure the density aftereffect for a wide range of adaptor densities (0-73 dots/deg2) and test densities (1.6, 6.4, and 25.6 dots/deg2). We found bidirectionality for all combinations of adaptor and test densities, consistent with our previous SDC results. This evidence supports the idea that there are multiple channels selective to texture density in human vision.

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.

Dynamic perspective cues enhance depth perception from motion parallax.

Motion parallax, the perception of depth resulting from an observer's self-movement, has almost always been studied with random dot textures in simplified orthographic rendering. Here we examine depth from motion parallax in more naturalistic conditions using textures with an overall 1/f spectrum and dynamic perspective rendering. We compared depth perception for orthographic and perspective rendering, using textures composed of two types of elements: random dots and Gabor micropatterns. Relative texture motion (shearing) with square wave corrugation patterns was synchronized to horizontal head movement. Four observers performed a two-alternative forced choice depth ordering task with monocular viewing, in which they reported which part of the texture appeared in front of the other. For both textures, depth perception was better with dynamic perspective than with orthographic rendering, particularly at larger depths. Depth ordering performance with naturalistic 1/f textures was slightly lower than with the random dots; however, with depth-related size scaling of the micropatterns, performance was comparable to that with random dots. We also examined the effects of removing each of the three cues that distinguish dynamic perspective from orthographic rendering: (a) small vertical displacements, (b) lateral gradients of speed across the corrugations, and (c) speed differences in rendered near versus far surfaces. Removal of any of the three cues impaired performance. In conclusion, depth ordering performance is enhanced by all of the dynamic perspective cues but not by using more naturalistic 1/f textures.

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.

Simultaneous density contrast is bidirectional.

Simultaneous density contrast, or SDC, is the phenomenon in which the perceived density of a textured region is altered by a surround of different density (Mackay, 1973). SDC provides an experimental tool to investigate mechanisms of density coding, yet has not been systematically examined. We measured SDC with a 2AFC staircase procedure in which human observers judged which of two patterns, one with and one without a surround, appeared more dense. We used a range of surround densities varying from very sparse to very dense (0-76.8 dots/deg2), and two center test densities (6.4 and 12.8 dots/deg2). Psychometric functions were used to estimate both the points of subjective equality (PSE) and their precision. Unexpectedly we find a bidirectional SDC effect across the five observers: Not only does a denser surround reduce perceived density of the center, but a sparser surround enhances its perceived density. We also show that SDC is not mediated by either contrast-contrast or spatial-frequency contrast. Our results suggest the presence of multiple channels selective for texture density, with lateral inhibitory interactions between them.

Categorically distinct types of receptive fields in early visual cortex.

In the visual cortex, distinct types of neurons have been identified based on cellular morphology, response to injected current, or expression of specific markers, but neurophysiological studies have revealed visual receptive field (RF) properties that appear to be on a continuum, with only two generally recognized classes: simple and complex. Most previous studies have characterized visual responses of neurons using stereotyped stimuli such as bars, gratings, or white noise and simple system identification approaches (e.g., reverse correlation). Here we estimate visual RF models of cortical neurons using visually rich natural image stimuli and regularized regression system identification methods and characterize their spatial tuning, temporal dynamics, spatiotemporal behavior, and spiking properties. We quantitatively demonstrate the existence of three functionally distinct categories of simple cells, distinguished by their degree of orientation selectivity (isotropic or oriented) and the nature of their output nonlinearity (expansive or compressive). In addition, these three types have differing average values of several other properties. Cells with nonoriented RFs tend to have smaller RFs, shorter response durations, no direction selectivity, and high reliability. Orientation-selective neurons with an expansive output nonlinearity have Gabor-like RFs, lower spontaneous activity and responsivity, and spiking responses with higher sparseness. Oriented RFs with a compressive nonlinearity are spatially nondescript and tend to show longer response latency. Our findings indicate multiple physiologically defined types of RFs beyond the simple/complex dichotomy, suggesting that cortical neurons may have more specialized functional roles rather than lying on a multidimensional continuum.

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

A fundamental task of the visual system is to extract figure-ground boundaries between images of objects, which in natural scenes are often defined not only by luminance differences but also by "second-order" contrast or texture differences. Responses to contrast modulation (CM) and other second-order stimuli have been extensively studied in human psychophysics, but the neuronal substrates of second-order responses in nonhuman primates remain poorly understood. In this study, we have recorded single neurons in area V2 of macaque monkeys, using both CM patterns as well as conventional luminance modulation (LM) gratings. CM stimuli were constructed from stationary sine wave grating carrier patterns, which were modulated by drifting envelope gratings of a lower spatial frequency. We found approximately one-third of visually responsive V2 neurons responded to CM stimuli with a pronounced selectivity to carrier spatial frequencies, and often orientations, that were clearly outside the neurons' passbands for LM gratings. These neurons were "form-cue invariant" in that their tuning to CM envelope spatial frequency and orientation was very similar to that for LM gratings. Neurons were tuned to carrier spatial frequencies that were typically 2-4 octaves higher than their optimal envelope spatial frequencies, similar to results from human psychophysics. These results are distinct from CM responses arising from surround suppression, but could be understood in terms of a filter-rectify-filter model. Such neurons could provide a functionally useful and explicit representation of segmentation boundaries as well as a plausible neural substrate for human perception of second-order boundaries.

Higher order image structure enables boundary segmentation in the absence of luminance or contrast cues.

Lower order image statistics, which can be described by an image's Fourier energy content, enable segmentation when they are different on either side of a boundary. We have previously demonstrated that the spatial distribution of the energy in an image (described by its higher order statistics or structure) could influence segmentation thresholds for contrast- and orientation-defined boundaries, even though it was the same on either side of the boundary and thus task irrelevant (Zavitz & Baker, 2013). Here we examined whether higher order statistics can also enable segmentation when boundaries are defined by differences in structure or density of texture elements. We used micropattern-based naturalistic synthetic textures to manipulate the sparseness, global phase alignment, and local phase alignment of carrier textures and measured segmentation thresholds based on forced-choice judgments of boundary orientation. We found that both global phase structure and sparseness, but not local phase alignment, enable segmentation and that local structure also has a small effect on segmentation thresholds in both cases. Simulations of a two-stage filter model with a compressive intermediate nonlinearity can reproduce the major features of the experimental data, segmenting boundaries defined by higher order statistics alone while capturing the influence of global image structure on segmentation thresholds.

Boundary segmentation from dynamic occlusion-based motion parallax.

Active observer movement results in retinal image motion that is highly dependent on the scene layout. This retinal motion, often called motion parallax, can yield significant information about the boundaries between objects and their relative depth differences. Previously we examined segmentation from shear-based motion parallax, which consists of only relative motion information. Here, we examine segmentation from dynamic occlusion-based motion parallax, which contains both relative motion and accretion-deletion. We utilized random dots whose motion was modulated with vertical low spatial frequency envelopes and synchronized to head movements (Head Sync), or recreated using previously recorded head movement data for the same stationary observer (Playback). Observers judged the orientation of a boundary between regions of oppositely moving dots in a 2AFC task. The results demonstrate that observers perform poorer when the stimulus motion is synchronized to head movement, particularly at smaller relative depths, even though that head movement provides significant information about depth. Both expansion-compression and accretion-deletion in isolation could support segmentation, albeit with reduced performance. Therefore, unlike our previous results for depth ordering, expansion-compression and accretion-deletion contribute similarly to segmentation. Furthermore, human observers do not appear to utilize depth information to improve segmentation performance.

Depth perception from dynamic occlusion in motion parallax: roles of expansion-compression versus accretion-deletion.

Motion parallax, or differential retinal image motion from observer movement, provides important information for depth perception. We previously measured the contribution of shear motion parallax to depth, which is only composed of relative motion information. Here, we examine the roles of relative motion and accretion-deletion information in dynamic occlusion motion parallax. Observers performed two-alternative forced choice depth-ordering tasks in response to low spatial frequency patterns of horizontal random dot motion that were synchronized to the observer's head movements. We examined conditions that isolated or combined expansion-compression and accretion-deletion across a range of simulated relative depths. At small depths, expansion-compression provided reliable depth perception while accretion-deletion had a minor contribution: When the two were in conflict, the perceived depth was dominated by expansion-compression. At larger depths in the cue-conflict experiment, accretion-deletion determined the depth-ordering performance. Accretion-deletion in isolation did not yield any percept of depth even though, in theory, it provided sufficient information for depth ordering. Thus, accretion-deletion can substantially enhance depth perception at larger depths but only in the presence of relative motion. The results indicate that expansion-compression contributes to depth from motion parallax across a broad range of depths whereas accretion-deletion contributes primarily at larger depths.

Texture sparseness, but not local phase structure, impairs second-order segmentation.

Texture boundary segmentation is typically thought to reflect a comparison of differences in Fourier energy (i.e. low-order texture statistics) on either side of a boundary. However in a previous study (Arsenault, Yoonessi, & Baker, 2011) we showed that the distribution of energy within a natural texture (i.e. its higher-order statistical structure) also influences segmentation of contrast boundaries. Here we examine the influence of specific higher-order texture statistics on segmentation of contrast- and orientation-defined boundaries. Using naturalistic synthetic textures to manipulate the sparseness, global phase structure, and local phase alignments of carrier textures, we measure segmentation thresholds based on forced-choice judgments of boundary orientation. We find a similar pattern of results for both contrast and orientation boundaries: (1) randomizing all structure by globally phase scrambling the texture reduces segmentation thresholds substantially, (2) decreasing sparseness also reduces thresholds, and (3) removing local phase alignments has little or no effect on segmentation thresholds. We show that a two-stage filter model with an intermediate compressive nonlinearity and expansive output nonlinearity can account for these data using synthetic textures. Furthermore, the model parameter fits obtained using synthetic textures also predict the segmentation thresholds presented in Arsenault, Yoonessi, and Baker (2011) for natural and phase-scrambled natural texture carriers.

Functional organization of envelope-responsive neurons in early visual cortex: organization of carrier tuning properties.

It is well established that visual cortex neurons having similar selectivity for orientation, direction of motion, ocular dominance, and other properties of first-order (luminance-defined) stimuli are clustered into a columnar organization. However, the cortical architecture of neuronal responses to second-order (contrast/texture-defined) stimuli is poorly understood. A useful second-order stimulus is a contrast envelope, consisting of a finely detailed pattern (carrier) whose contrast varies on a coarse spatial scale (envelope). In this study, we analyzed the cortical organization of carrier tuning properties of neurons, which responded to contrast-modulated stimuli. We examined whether neurons tuned to similar carrier properties are clustered spatially and whether such spatial clusters are arranged in columns. To address these questions, we recorded single-unit activity, multiunit activity, and local field potentials simultaneously from area 18 of anesthetized cats, using single-channel microelectrodes and multielectrode arrays. Our data showed that neurons tuned to similar carrier spatial frequency are distributed in a highly clustered manner; neurons tuned to similar carrier orientation are also significantly clustered. Neurons along linear arrays perpendicular to the brain surface always exhibited similar optimal carrier spatial frequency, indicating a columnar organization. Multi-pronged tetrode recordings indicated that the diameter of these columns is ≥450 µm. Optimal carrier orientation was also significantly clustered but with finer-grain organization and greater scatter. These results indicate a fine anatomical structure of cortical organization of second-order information processing and suggest that there are probably more maps in cat area 18 than previously believed.

Motion-defined contour processing in the early visual cortex.

From our daily experience, it is very clear that relative motion cues can contribute to correctly identifying object boundaries and perceiving depth. Motion-defined contours are not only generated by the motion of objects in a scene but also by the movement of an observer's head and body (motion parallax). However, the neural mechanism involved in detecting these contours is still unknown. To explore this mechanism, we extracellularly recorded visual responses of area 18 neurons in anesthetized and paralyzed cats. The goal of this study was to determine if motion-defined contours could be detected by neurons that have been previously shown to detect luminance-, texture-, and contrast-defined contours cue invariantly. Motion-defined contour stimuli were generated by modulating the velocity of high spatial frequency sinusoidal luminance gratings (carrier gratings) by a moving squarewave envelope. The carrier gratings were outside the luminance passband of a neuron, such that presence of the carrier alone within the receptive field did not elicit a response. Most neurons that responded to contrast-defined contours also responded to motion-defined contours. The orientation and direction selectivity of these neurons for motion-defined contours was similar to that of luminance gratings. A given neuron also exhibited similar selectivity for the spatial frequency of the carrier gratings of contrast- and motion-defined contours. These results suggest that different second-order contours are detected in a form-cue invariant manner, through a common neural mechanism in area 18.