The Representation of Orientation in Macaque V2: Four Stripes Not Three

Orientation selectivity mapping in the visual cortex

The orientation map is one of the most well-studied functional maps of the visual cortex. However, results from the literature are of different qualities. Clear boundaries among different orientation domains and blurred uncertain distinctions were shown in different studies. These unclear imaging results will lead to an inaccuracy in depicting cortical structures, and the lack of consideration in experimental design will also lead to biased depictions of the cortical features. How we accurately define orientation domains will impact the entire field of research. In this study, we test how spatial frequency (SF), stimulus size, location, chromatic, and data processing methods affect the orientation functional maps (including a large area of dorsal V4, and parts of dorsal V1) acquired by intrinsic signal optical imaging. Our results indicate that, for large imaging fields, large grating stimuli with mixed SF components should be considered to acquire the orientation map. A diffusion model image enhancement based on the difference map could further improve the map quality. In addition, the similar outcomes of achromatic and chromatic gratings indicate two alternative types of afferents from LGN, pooling in V1 to generate cue-invariant orientation selectivity.

Primate V2 Receptive Fields Derived from Anatomically Identified Large-Scale V1 Inputs

In the primate visual system, visual object recognition involves a series of cortical areas arranged hierarchically along the ventral visual pathway. As information flows through this hierarchy, neurons become progressively tuned to more complex image features. The circuit mechanisms and computations underlying the increasing complexity of these receptive fields (RFs) remain unidentified. To understand how this complexity emerges in the secondary visual area (V2), we investigated the functional organization of inputs from the primary visual cortex (V1) to V2 by combining retrograde anatomical tracing of these inputs with functional imaging of feature maps in macaque monkey V1 and V2. We found that V1 neurons sending inputs to single V2 orientation columns have a broad range of preferred orientations, but are strongly biased towards the orientation represented at the injected V2 site. For each V2 site, we then constructed a feedforward model based on the linear combination of its anatomically-identified large-scale V1 inputs, and studied the response proprieties of the generated V2 RFs. We found that V2 RFs derived from the linear feedforward model were either elongated versions of V1 filters or had spatially complex structures. These modeled RFs predicted V2 neuron responses to oriented grating stimuli with high accuracy. Remarkably, this simple model also explained the greater selectivity to naturalistic textures of V2 cells compared to their V1 input cells. Our results demonstrate that simple linear combinations of feedforward inputs can account for the orientation selectivity and texture sensitivity of V2 RFs.

Primate V2 Receptive Fields Derived from Anatomically Identified Large-Scale V1 Inputs

In the primate visual system, visual object recognition involves a series of cortical areas arranged hierarchically along the ventral visual pathway. As information flows through this hierarchy, neurons become progressively tuned to more complex image features. The circuit mechanisms and computations underlying the increasing complexity of these receptive fields (RFs) remain unidentified. To understand how this complexity emerges in the secondary visual area (V2), we investigated the functional organization of inputs from the primary visual cortex (V1) to V2 by combining retrograde anatomical tracing of these inputs with functional imaging of feature maps in macaque monkey V1 and V2. We found that V1 neurons sending inputs to single V2 orientation columns have a broad range of preferred orientations, but are strongly biased towards the orientation represented at the injected V2 site. For each V2 site, we then constructed a feedforward model based on the linear combination of its anatomically-identified large-scale V1 inputs, and studied the response proprieties of the generated V2 RFs. We found that V2 RFs derived from the linear feedforward model were either elongated versions of V1 filters or had spatially complex structures. These modeled RFs predicted V2 neuron responses to oriented grating stimuli with high accuracy. Remarkably, this simple model also explained the greater selectivity to naturalistic textures of V2 cells compared to their V1 input cells. Our results demonstrate that simple linear combinations of feedforward inputs can account for the orientation selectivity and texture sensitivity of V2 RFs.

Spatial frequency representation in V2 and V4 of macaque monkey

Spatial frequency (SF) is an important attribute in the visual scene and is a defining feature of visual processing channels. However, there remain many unsolved questions about how extrastriate areas in primate visual cortex code this fundamental information. Here, using intrinsic signal optical imaging in visual areas of V2 and V4 of macaque monkeys, we quantify the relationship between SF maps and (1) visual topography and (2) color and orientation maps. We find that in orientation regions, low to high SF is mapped orthogonally to orientation; in color regions, which are reported to contain orthogonal axes of color and lightness, low SFs tend to be represented more frequently than high SFs. This supports a population-based SF fluctuation related to the 'color/orientation' organizations. We propose a generalized hypercolumn model across cortical areas, comprised of two orthogonal parameters with additional parameters.

Early Emergence of Solid Shape Coding in Natural and Deep Network Vision

Area V4 is the first object-specific processing stage in the ventral visual pathway, just as area MT is the first motion-specific processing stage in the dorsal pathway. For almost 50 years, coding of object shape in V4 has been studied and conceived in terms of flat pattern processing, given its early position in the transformation of 2D visual images. Here, however, in awake monkey recording experiments, we found that roughly half of V4 neurons are more tuned and responsive to solid, 3D shape-in-depth, as conveyed by shading, specularity, reflection, refraction, or disparity cues in images. Using 2-photon functional microscopy, we found that flat- and solid-preferring neurons were segregated into separate modules across the surface of area V4. These findings should impact early shape-processing theories and models, which have focused on 2D pattern processing. In fact, our analyses of early object processing in AlexNet, a standard visual deep network, revealed a similar distribution of sensitivities to flat and solid shape in layer 3. Early processing of solid shape, in parallel with flat shape, could represent a computational advantage discovered by both primate brain evolution and deep-network training.

"Multiplexing" cells of the visual cortex and the timing enigma of the binding problem

In this opinion essay, I address the perennial binding problem, that is to say of how independently processed visual attributes such as form, colour and motion are brought together to give us a unified and holistic picture of the visual world. A solution to this central issue in neurobiology remains as elusive as ever. No one knows today how it is implemented. The issue is not a new one and, though discussed most commonly in the context of the visual brain, it is not unique to it either. Karl Lashley summarized it well years ago when he wrote that a critical problem for brain studies is to understand how "the specialized areas of the cerebral cortex interact to provide the integration evident in thought and behaviour" (Lashley, 1931).

Anatomy and Physiology of Macaque Visual Cortical Areas V1, V2, and V5/MT: Bases for Biologically Realistic Models

The cerebral cortex of primates encompasses multiple anatomically and physiologically distinct areas processing visual information. Areas V1, V2, and V5/MT are conserved across mammals and are central for visual behavior. To facilitate the generation of biologically accurate computational models of primate early visual processing, here we provide an overview of over 350 published studies of these three areas in the genus Macaca, whose visual system provides the closest model for human vision. The literature reports 14 anatomical connection types from the lateral geniculate nucleus of the thalamus to V1 having distinct layers of origin or termination, and 194 connection types between V1, V2, and V5, forming multiple parallel and interacting visual processing streams. Moreover, within V1, there are reports of 286 and 120 types of intrinsic excitatory and inhibitory connections, respectively. Physiologically, tuning of neuronal responses to 11 types of visual stimulus parameters has been consistently reported. Overall, the optimal spatial frequency (SF) of constituent neurons decreases with cortical hierarchy. Moreover, V5 neurons are distinct from neurons in other areas for their higher direction selectivity, higher contrast sensitivity, higher temporal frequency tuning, and wider SF bandwidth. We also discuss currently unavailable data that could be useful for biologically accurate models.

Neuronal response properties across cytochrome oxidase stripes in primate V2

Cytochrome oxidase histochemistry reveals large-scale cortical modules in area V2 of primates known as thick, thin, and interstripes. Anatomical, electrophysiological, and tracing studies suggest that V2 cytochrome oxidase stripes participate in functionally distinct streams of visual information processing. However, there is controversy whether the different V2 compartments indeed correlate with specialized neuronal response properties. We used multiple-electrode arrays (16 × 2, 8 × 4 and 4 × 4 matrices) to simultaneously record the spiking activity (N = 190 single units) across distinct V2 stripes in anesthetized and paralyzed capuchin monkeys (N = 3 animals, 6 hemispheres). Visual stimulation consisted of moving bars and full-field gratings with different contrasts, orientations, directions of motion, spatial frequencies, velocities, and color contrasts. Interstripe neurons exhibited the strongest orientation and direction selectivities compared to the thick and thin stripes, with relatively stronger coding for orientation. Additionally, they responded best to higher spatial frequencies and to lower stimulus velocities. Thin stripes showed the highest proportion (80%) of neurons selective to color contrast (compared to 47% and 21% for thick and interstripes, respectively). The great majority of the color selective cells (86%) were also orientation selective. Additionally, thin stripe neurons continued to increase their firing rate for stimulus contrasts above 50%, while thick and interstripe neurons already exhibited some degree of response saturation at this point. Thick stripes best coded for lower spatial frequencies and higher stimulus velocities. In conclusion, V2 CytOx stripes exhibit a mixed degree of segregation and integration of information processing, shedding light into the early mechanisms of vision.

Columnar organization of mid-spectral and end-spectral hue preferences in human visual cortex

Multiple color-selective areas have been described in visual cortex, in both humans and non-human primates. In macaques, hue-selective columns have been reported in several areas. In V2, it has been proposed that such hue-selective columns are mapped so as to mirror the order of wavelength through the visible spectrum, within thin-type stripes. Other studies have suggested a neural segregation of mid-spectral vs. end-spectral hue preferences (e.g. red and blue vs. green and yellow), within thin- and thick-type stripes, respectively. This latter segregation could reduce the spatial 'blur' due to chromatic aberration in the encoding of fine spatial details in the thick-type stripes. To distinguish between these and related models, we tested the organization of hue preferences in human visual cortex using fMRI at high spatial resolution. We used a high field (7T) scanner in humans (n = 7), measuring responses to four independent hues, including end-spectral (i.e. red-gray and blue-gray) and mid-spectral (i.e. green-gray and yellow-gray) isoluminant gratings, and also relative to achromatic luminance-varying (control) stimuli. In each subject, thin- and thick-type columns in V2 and V3 were localized using an independent set of stimuli and scans. We found distinct hue-selective differences along the dimension of mid-vs. end-spectral hues, in striate and early extrastriate visual cortex. First, as reported previously in macaques, V1 responded more strongly to end-spectral hues, compared to mid-spectral hues. Second, the color-selective thin-type stripes in V2 and V3 showed a greater response to end- and mid-spectral hues, relative to luminance-varying gratings. Third, thick-type stripes in V2/V3 showed a significantly stronger response to mid-spectral (compared to end-spectral) hues. Fourth, in the higher-tier color-selective area in occipital temporal cortex (n = 4), responses to all four hues were statistically equivalent to each other. These results suggest that early visual cortex segregates the processing of mid-vs. end-spectral hues, perhaps to counter the challenging optical constraint of chromatic aberration.

Specificity of V1-V2 orientation networks in the primate visual cortex

The computation of texture and shape involves integration of features of various orientations. Orientation networks within V1 tend to involve cells which share similar orientation selectivity. However, emergent properties in V2 require the integration of multiple orientations. We now show that, unlike interactions within V1, V1-V2 orientation interactions are much less synchronized and are not necessarily orientation dependent. We find V1-V2 orientation networks are of two types: a more tightly synchronized, orientation-preserving network and a less synchronized orientation-diverse network. We suggest that such diversity of V1-V2 interactions underlies the spatial and functional integration required for computation of higher order contour and shape in V2.