title: Coding of the contrasts in natural images by visual cortex (V1) neurons: a Bayesian approach.
abstract: Individual V1 neurons respond dynamically over only limited ranges of stimulus contrasts, yet we can discriminate contrasts over a wide range. Different V1 neurons cover different parts of the contrast range, and the information they provide must be pooled somehow. We describe a probabilistic pooling model that shows that populations of neurons with contrast responses like those in cat and monkey V1 would most accurately code contrasts in the range actually found in natural scenes. The pooling equation is similar to Bayes's equation; however, explicit inclusion of prior probabilities in the inference increases coding accuracy only slightly.
authors.affiliation: Department of Physiology, University of Cambridge, Downing Street, Cambridge CB2 3EG, UK. mc325@cam.ac.uk
authors.forename: Mazviita
authors.initials: M
authors.lastname: Chirimuuta
authors.forename: Philip L
authors.initials: PL
authors.lastname: Clatworthy
authors.forename: David J
authors.initials: DJ
authors.lastname: Tolhurst
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D002415:Cats; D015350:Contrast Sensitivity / Q000502:physiology*; D000882:Haplorhini; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D014793:Visual Cortex / Q000166:cytology / Q000502:physiology*