We address the technical challenges involved in combining key features from several theories of the visual cortex in a single computational model. The resulting model is a hierarchical Bayesian network factored into modular component networks implementing variable-order Markov models. Each component network has an associated receptive field corresponding to components in the level directly below it in the hierarchy. The variable-order Markov models account for features that are invariant to naturally occurring transformations in their inputs. These invariant features support efficient generalization and produce increasingly stable, persistent representations as we ascend the hierarchy. The receptive...
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