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  Statistical Models of Linear and Nonlinear Contextual Interactions in Early Visual Processing

Coen-Cagli, R., Dayan, P., & Schwartz, O. (2010). Statistical Models of Linear and Nonlinear Contextual Interactions in Early Visual Processing. In Y. Bengio, D. Schuurmans, J. Lafferty, C. Williams, & A. Culotta (Eds.), Advances in Neural Information Processing Systems 22 (pp. 369-377). Red Hook, NY, USA: Curran.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0004-D2DA-9 Version Permalink: https://hdl.handle.net/21.11116/0000-0004-D2DB-8
Genre: Conference Paper

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 Creators:
Coen-Cagli, R, Author
Dayan, P1, Author           
Schwartz, O, Author
Affiliations:
1External Organizations, ou_persistent22              

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 Abstract: A central hypothesis about early visual processing is that it represents inputs in a coordinate system matched to the statistics of natural scenes. Simple versions of this lead to Gabor-like receptive fields and divisive gain modulation from local surrounds; these have led to influential neural and psychological models of visual processing. However, these accounts are based on an incomplete view of the visual context surrounding each point. Here, we consider an approximate model of linear and non-linear correlations between the responses of spatially distributed Gabor-like receptive fields, which, when trained on an ensemble of natural scenes, unifies a range of spatial context effects. The full model accounts for neural surround data in primary visual cortex (V1), provides a statistical foundation for perceptual phenomena associated with Lis (2002) hypothesis that V1 builds a saliency map, and fits data on the tilt illusion.

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 Dates: 2010-04
 Publication Status: Issued
 Pages: -
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Title: 23rd Annual Conference on Neural Information Processing Systems (NIPS 2009)
Place of Event: Vancouver, BC, Canada
Start-/End Date: 2009-12-07 - 2009-12-10

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Title: Advances in Neural Information Processing Systems 22
Source Genre: Proceedings
 Creator(s):
Bengio, Y, Editor
Schuurmans, D, Editor
Lafferty, J, Editor
Williams, C, Editor
Culotta, A, Editor
Affiliations:
-
Publ. Info: Red Hook, NY, USA : Curran
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 369 - 377 Identifier: ISBN: 978-1-615-67911-9