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dc.contributor.authorKouh, Minjoon
dc.contributor.authorRiesenhuber, Maximilian
dc.date.accessioned2005-12-20T22:01:32Z
dc.date.available2005-12-20T22:01:32Z
dc.date.issued2003-09-08
dc.identifier.otherMIT-CSAIL-TR-2003-019
dc.identifier.otherAIM-2003-021
dc.identifier.otherCBCL-231
dc.identifier.urihttp://hdl.handle.net/1721.1/30424
dc.description.abstractThe question of how shape is represented is of central interest to understanding visual processing in cortex. While tuning properties of the cells in early part of the ventral visual stream, thought to be responsible for object recognition in the primate, are comparatively well understood, several different theories have been proposed regarding tuning in higher visual areas, such as V4. We used the model of object recognition in cortex presented by Riesenhuber and Poggio (1999), where more complex shape tuning in higher layers is the result of combining afferent inputs tuned to simpler features, and compared the tuning properties of model units in intermediate layers to those of V4 neurons from the literature. In particular, we investigated the issue of shape representation in visual area V1 and V4 using oriented bars and various types of gratings (polar, hyperbolic, and Cartesian), as used in several physiology experiments. Our computational model was able to reproduce several physiological findings, such as the broadening distribution of the orientation bandwidths and the emergence of a bias toward non-Cartesian stimuli. Interestingly, the simulation results suggest that some V4 neurons receive input from afferents with spatially separated receptive fields, leading to experimentally testable predictions. However, the simulations also show that the stimulus set of Cartesian and non-Cartesian gratings is not sufficiently complex to probe shape tuning in higher areas, necessitating the use of more complex stimulus sets.
dc.format.extent14 p.
dc.format.extent20150764 bytes
dc.format.extent1258167 bytes
dc.format.mimetypeapplication/postscript
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.relation.ispartofseriesMassachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory
dc.subjectAI
dc.subjectShape Tuning
dc.subjectShape Representation
dc.subjectFeatures
dc.subjectHMAX
dc.subjectVisual Cortex
dc.subjectGratings
dc.subjectV4
dc.titleInvestigating shape representation in area V4 with HMAX: Orientation and Grating selectivities


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