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Strategies of shape representation in macaque visual area V2

Published online by Cambridge University Press:  23 September 2003

JAY HEGDÉ
Affiliation:
Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis
DAVID C. VAN ESSEN
Affiliation:
Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis

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.

Type
Research Article
Copyright
© 2003 Cambridge University Press

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