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.
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