Statistical models of linear and non-linear contextual interactions in early visual processing.
Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference
369 - 377.
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 Li's (2002) hypothesis that V1 builds a saliency map, and fits data on the tilt illusion.
|Title:||Statistical models of linear and non-linear contextual interactions in early visual processing|
|UCL classification:||UCL > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neuroscience Unit|
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