title: Multinomial Bayesian learning for modeling classical and nonclassical receptive field properties.
abstract: We study the interplay of Bayesian inference and natural image learning in a hierarchical vision system, in relation to the response properties of early visual cortex. We particularly focus on a Bayesian network with multinomial variables that can represent discrete feature spaces similar to hypercolumns combining minicolumns, enforce sparsity of activation to learn efficient representations, and explain divisive normalization. We demonstrate that maximal-likelihood learning using sampling-based Bayesian inference gives rise to classical receptive field properties similar to V1 simple cells and V2 cells, while inference performed on the trained network yields nonclassical context-dependent response properties such as cross-orientation suppression and filling in. Comparison with known physiological properties reveals some qualitative and quantitative similarities.
authors.affiliation: Brain Science Institute, RIKEN, and PRESTO, Japan Science and Technology Agency, Wako-shi, Saitama 351-0198, Japan. hahosoya@brain.riken.jp
authors.forename: Haruo
authors.initials: H
authors.lastname: Hosoya
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D014793:Visual Cortex / Q000502:physiology*; D014794:Visual Fields; D014796:Visual Perception