  Research has shown that neurons within the brain are selective to certain
stimuli. For example, the fusiform face area (FFA) region is known by
neuroscientists to selectively activate when people see faces over non-face
objects. However, the mechanisms by which the primary visual system directs
information to the correct higher levels of the brain are currently unknown. In
our work, we mimic several high-level neural mechanisms of perception by
creating a novel computational model that incorporates lateral and top down
feedback in the form of hierarchical competition. Not only do we show that
these elements can help explain the information flow and selectivity of high
level areas within the brain, we also demonstrate that these neural mechanisms
provide the foundation of a novel classification framework that rivals
traditional supervised learning in computer vision. Additionally, we present
both quantitative and qualitative results that demonstrate that our generative
framework is consistent with neurological themes and enables simple, yet robust
category level classification.
