title: Statistical models of natural images and cortical visual representation.
abstract: 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.
authors.affiliation: Department of Mathematics and Statistics, Department of Computer Science and HIIT, Department of Psychology, University of Helsinki.
authors.forename: Aapo
authors.initials: A
authors.lastname: Hyvärinen
mesh_terms: D006801:Humans; D007858:Learning / Q000502:physiology; D008959:Models, Neurological; D015233:Models, Statistical; D009474:Neurons / Q000502:physiology; D010775:Photic Stimulation; D014793:Visual Cortex / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology
keywords: Computational models; Natural image statistics; Natural scenes; Probabilistic models; Vision