To recognize objects, the human visual system processes information through a network of hierarchically organized brain regions. Many neurocomputational models have modeled this hierarchical structure, but they have often used hand-crafted features. According to the linear efficient coding hypothesis, the goal of the early visual pathway is to capture the statistical structure of sensory stimuli, removing redundancy, and factoring the input into independent features. In this work, we use a hierarchical ICA algorithm to automatically learn the visual features that account for early visual cortex. We then continue modeling the object recognition pathway using Gnostic Fields, a theory for how the brain does object categorization, in which brain regions devoted to classifying mutually-exclusive categories exist near the top of sensory processing hierarchies. The whole biologically-inspired model not only allows us to develop representations similar to those in primary visual cortex, but also to perform well on standard object recognition benchmarks.