title: Predicting cortical dark/bright asymmetries from natural image statistics and early visual transforms.
abstract: The nervous system has evolved in an environment with structure and predictability. One of the ubiquitous principles of sensory systems is the creation of circuits that capitalize on this predictability. Previous work has identified predictable non-uniformities in the distributions of basic visual features in natural images that are relevant to the encoding tasks of the visual system. Here, we report that the well-established statistical distributions of visual features--such as visual contrast, spatial scale, and depth--differ between bright and dark image components. Following this analysis, we go on to trace how these differences in natural images translate into different patterns of cortical input that arise from the separate bright (ON) and dark (OFF) pathways originating in the retina. We use models of these early visual pathways to transform natural images into statistical patterns of cortical input. The models include the receptive fields and non-linear response properties of the magnocellular (M) and parvocellular (P) pathways, with their ON and OFF pathway divisions. The results indicate that there are regularities in visual cortical input beyond those that have previously been appreciated from the direct analysis of natural images. In particular, several dark/bright asymmetries provide a potential account for recently discovered asymmetries in how the brain processes visual features, such as violations of classic energy-type models. On the basis of our analysis, we expect that the dark/bright dichotomy in natural images plays a key role in the generation of both cortical and perceptual asymmetries.
authors.affiliation: Department of Psychology, Stanford University, Stanford, California, United States of America.
authors.forename: Emily A
authors.initials: EA
authors.lastname: Cooper
authors.affiliation: Department of Psychology, Stanford University, Stanford, California, United States of America.
authors.forename: Anthony M
authors.initials: AM
authors.lastname: Norcia
mesh_terms: D000818:Animals; D002138:Calibration; D003198:Computer Simulation; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008959:Models, Neurological; D015233:Models, Statistical; D009474:Neurons / Q000502:physiology; D016011:Normal Distribution; D010363:Pattern Recognition, Automated; D010775:Photic Stimulation; D011336:Probability; D012160:Retina / Q000502:physiology*; D012165:Retinal Ganglion Cells / Q000502:physiology; D014785:Vision, Ocular; D014793:Visual Cortex / Q000502:physiology*; D014795:Visual Pathways / Q000502:physiology; D014796:Visual Perception