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Research has shown that neurons within the brain are selective to certainstimuli. For example, the fusiform face area (FFA) region is known byneuroscientists to selectively activate when people see faces over non-faceobjects. However, the mechanisms by which the primary visual system directsinformation to the correct higher levels of the brain are currently unknown. Inour work, we mimic several high-level neural mechanisms of perception bycreating a novel computational model that incorporates lateral and top downfeedback in the form of hierarchical competition. Not only do we show thatthese elements can help explain the information flow and selectivity of highlevel areas within the brain, we also demonstrate that these neural mechanismsprovide the foundation of a novel classification framework that rivalstraditional supervised learning in computer vision. Additionally, we presentboth quantitative and qualitative results that demonstrate that our generativeframework is consistent with neurological themes and enables simple, yet robustcategory level classification.

The Selectivity and Competition of the Mind's Eye in Visual Perception