The neocortex appears to be a very efficient, uniformly structured, and hierarchical computational system [25], [23], [24]. Researchers have made significant efforts to model intelligent systems that mimic these neocortical properties to perform a broad variety of pattern recognition and learning tasks. Unfortunately, many of these systems have drifted away from their cortical origins and incorporate or rely on attributes and algorithms that are not biologically plausible. In contrast, this paper describes a model for an intelligent system that is motivated by the properties of cortical columns, which can be viewed as the basic functional unit of the neocortex [35], [16]. Our model extends predictability minimization [30] to mimic the behavior of cortical columns and incorporates neocortical properties such as hierarchy, structural uniformity, and plasticity, and enables adaptive, hierarchical independent feature detection. Initial results for an unsupervised learning task-identifying independent features in image data-are quite promising, both in a single-level and a hierarchical organization modeled after the visual cortex. The model is also able to forget learned patterns that no longer appear in the dataset, demonstrating its adaptivity, resilience, and stability under changing input conditions.
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