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Statistical Models of Natural Images and Cortical Visual Representation
Author(s) -
Hyvärinen Aapo
Publication year - 2010
Publication title -
topics in cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.191
H-Index - 56
eISSN - 1756-8765
pISSN - 1756-8757
DOI - 10.1111/j.1756-8765.2009.01057.x
Subject(s) - receptive field , computer science , gestalt psychology , surround suppression , artificial intelligence , neural coding , representation (politics) , visual cortex , inference , statistical inference , coding (social sciences) , pattern recognition (psychology) , computational neuroscience , neuroscience , statistical model , computational model , visual perception , cognitive science , psychology , perception , mathematics , statistics , politics , political science , law
A fundamental question in visual neuroscience is: Why are the response properties of visual neurons as they are? A modern approach to this problem emphasizes the importance of adaptation to ecologically valid input, and it proceeds by modeling statistical regularities in ecologically valid visual input (natural images). A seminal model was linear sparse coding, which is equivalent to independent component analysis (ICA), and provided a very good description of the receptive fields of simple cells. Further models based on modeling residual dependencies of the ‘‘independent” components have later been introduced. These models lead to emergence of further properties of visual neurons: the complex cell receptive fields, the spatial organization of the cells, and some surround suppression and Gestalt effects. So far, these models have concentrated on the response properties of neurons, but they hold great potential to model various forms of inference and learning.