Modelling object perception in cortex: Hierarchical Bayesian networks and belief propagation

Abstract

Hierarchical generative models and Bayesian belief propagation have been shown to provide a theoretical framework that can account for perceptual processes, including feedback modulation. The framework explains both psychophysical and physiological experimental data and maps well onto the hierarchical distributed cortical anatomy. The complexity required to model cortical processes makes inference, even using approximate methods, very computationally expensive. Thus, existing models are typically limited to tree-structured networks with no loops, use small toy examples or fail to account for certain perceptual aspects such as invariance to transformations or feedback reconstruction.