Nature 329, 438 - 441 (01 October 1987); doi:10.1038/329438a0

Endstopped neurons in the visual cortex as a substrate for calculating curvature

Allan Dobbins, Steven W. Zucker & Max S. Cynader^*

Computer Vision and Robotics Laboratory, McGill Research Centre for Intelligent Machines, McGill University, 3480 University Street, Montréal, Québec H3A 2A7, Canada
^* Departments of Physiology, Biophysics and Psychology, Dalhousie University, Halifax, Nova Scotia B3H4J1, Canada.

Neurons in the visual cortex typically respond selectively to the orientation, and velocity and direction of movement, of moving-bar stimuli. These responses are generally thought to provide information about the orientation and position of lines and edges in the visual field. Some cells are also endstopped, that is selective for bars of specific lengths. Hubel and Wiesel first observed that endstopped hypercomplex cells¹ could respond to curved stimuli and suggested they might be involved in detection of curvature, but the exact relationship between endstopping and curvature has never been determined. We present here a mathematical model relating endstopping to curvature in which the difference in response of two simple cells gives rise to endstopping and varies in proportion to curvature. We also provide physiological evidence that endstopped cells in area 17 of the cat visual cortex are selective for curvature, whereas non-endstopped cells are not, and that some are selective for the sign of curvature. The prevailing view of edge and curve determination is that orientations are selected locally by the class of simple cortical cells³ and then integrated to form global curves. We have developed a computational theory of orientation selection^4,5 which shows that measurements of orientation obtained by simple cells are not sufficient because there will be strong, incorrect responses from cells whose receptive fields (RFs) span distinct curves (Fig. 1). If estimates of curvature are available, however, these inappropriate responses can be eliminated. Curvature provides the key to structuring the network that underlies our theory and distinguishes it from previous lateral inhibition schemes^6,7.

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