Strategies of shape representation in macaque visual area V2

Abstract

Contours and surface textures provide powerful cues used in image segmentation and the analysis of object shape.To learn more about how the visual system extracts and represents these visual cues, we studied the responses of V2 neurons in awake, fixating monkeys to complex contour stimuli (angles, intersections, arcs, and circles) and texture patterns such as non-Cartesian gratings, along with conventional bars and sinusoidal gratings. Substantial proportions of V2 cells conveyed information about many contour and texture characteristics associated with our stimuli, including shape, size, orientation, and spatial frequency. However, the cells differed considerably in terms of their degree of selectivity for the various stimulus characteristics. On average, V2 cells responded better to grating stimuli but were more selective for contour stimuli. Metric multidimensional scaling and principal components analysis showed that, as a population, V2 cells show strong correlations in how they respond to different stimulus types. The first two and five principal components accounted for 69% and 85% of the overall response variation, respectively, suggesting that the response correlations simplified the population representation of shape information with relatively little loss of information. Moreover, smaller random subsets of the population carried response correlation patterns very similar to the population as a whole, indicating that the response correlations were a widespread property of V2 cells. Thus, V2 cells extract information about a number of higher order shape cues related to contours and surface textures and about similarities among many of these shape cues. This may reflect an efficient strategy of representing cues for image segmentation and object shape using finite neuronal resources.

How cortical neurons help us see: visual recognition in the human brain

“…The current line of thought is that shape preferences and tolerance to object transformations build up gradually as we ascend the visual hierarchy (24,85,107,108). Consistent with this notion, neurons in areas V2 and V4 typically show larger receptive fields and responses to more complex shapes than do neurons in V1 but smaller receptive fields and less complexity and tolerance to object transformations than neurons in the ITC (109)(110)(111)(112). Much more work is needed to elucidate the computations that lead to shape recognition along the ventral visual pathway.…”

Detection and recognition of angular frequency patterns

“…Primate neurophysiology indicates that there are populations of V2 neurons selectively sensitive to angles (Anzai, Peng, & VanEssen, 2007;Hegdé & Van Essen, 2003). In addition, recent fMRI evidence has shown that the human parahippocampal place area (PPA) responds strongly to triangles, squares, and hexagons, but not to circles or dodecagons (Nasr, Echavarria, & Tootell, 2014).…”

“…This indicates that a more complete representation of object shapes must include contours incorporating sharp angles. In support of this, macaque V2 has also been reported to contain groups of neurons most sensitive to a range of angles (Anzai, Peng, & VanEssen, 2007;Hegdé & Van Essen, 2003). Therefore, the goal of this paper is to introduce a novel class of visual shapes that are formally analogous to RF patterns, but differ in being defined by sharp angles indicative of polygons.…”

“…Based on these studies, a recent neural model of RF pattern analysis has posited that contour orientation information from V1 is combined into curvature representations in V2, and these are then globally pooled in V4 to produce channels for different RF patterns (Wilson & Wilkinson, 2014). In support of this, V2 has been shown to contain neurons tuned for contour curvature (Anzai, Peng, & VanEssen, 2007;Hegdé & Van Essen, 2003). Analysis of V4 responses has also supported a sparse code for curved shapes based on curvature extrema (Carlson et al, 2011).…”

Nonlinear and higher-order approaches to the encoding of natural scenes

“…However, an indirect approximative test of the selectivity can be performed by considering the responses of measured and simulated nonlinear neurons to large test set of stimuli, and compare these to the responses of linear neurons. Extended test sets like the one used in Figure 16 have been employed for the analysis of V2 and V4 neurons (Gallant et al 1993;Hegde & van Essen 2003). We have used simulated nonlinear neurons that have been learned in the two-stage scheme shown in Figure 3 and described in Zetzsche & Röhrbein (2001) in a version with two resolution layers, four orientations, and even-and odd-symmetric filters, and applied these nonlinear neurons to the stimulus test set.…”