Generalization Over Contrast and Mirror Reversal, But not Figure-ground Reversal, in an "Edge-based" Model of IT Neurons
نویسنده
چکیده
Baylis & Driver [1] have recently presented data on the response of neurons in macaque inferotemporal cortex (IT) to various stimulus transformations. They report that neurons can generalize over contrast and mirror reversal, but not over figure-ground reversal. This finding is taken to demonstrate that “the selectivity of IT neurons is not determined simply by the distinctive contours in a display, contrary to simple edge-based models of shape recognition”, citing our recently presented model of object recognition in cortex [3]. In this memo, I show that the main effects of the experiment can be obtained by performing the appropriate simulations in our simple feedforwardmodel. This suggests for IT cell tuning that the possible contributions of explicit edge assignment processes postulated in [1] might be smaller than expected. Copyright c Massachusetts Institute of Technology, 2001 This report describes research done within the Center for Biological & Computational Learning in the Department of Brain & Cognitive Sciences and in the Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. This research was sponsored by grants from: Office of Naval Research (DARPA) under contract No. N00014-00-10907, National Science Foundation (ITR) under contract No. IIS-0085836, National Science Foundation (KDI) under contract No. DMS-9872936, and National Science Foundation under contract No. IIS-9800032. Additional support was provided by: AT&T, Central Research Institute of Electric Power Industry, Center for e-Business (MIT), Eastman Kodak Company, DaimlerChrysler AG, Compaq, Honda R&D Co., Ltd., ITRI, Komatsu Ltd., Merrill-Lynch,Mitsubishi Corporation, NEC Fund, Nippon Telegraph& Telephone, Oxygen, Siemens Corporate Research, Inc., Sumitomo Metal Industries, Toyota Motor Corporation, WatchVision Co., Ltd., and The Whitaker Foundation. M.R. is supported by a McDonnell-Pew Award in Cognitive Neuroscience.
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