Although enormous progress has been made in the collection and assimilation of data about the physiological properties and connectivity of cortical neurons, the data are not sufficient to derive a computational theory in a purely bottom-up fashion. The circuits of the neocortex are bewildering in their complexity and anatomical detail. Understanding the computational and information processing roles of cortical circuitry is one of the outstanding problems in neuroscience. We also discuss how the theory and the circuit can be extended to explain cortical features that are not explained by the current model and describe testable predictions that can be derived from the model. We describe the pattern recognition capabilities of HTM networks and demonstrate the application of the derived circuits for modeling the subjective contour effect. The combination of these two constraints suggests a theoretically derived interpretation for many anatomical and physiological features and predicts several others. Anatomical data provide a contrasting set of organizational constraints. Bayesian belief propagation equations for such an HTM node define a set of functional constraints for a neuronal implementation. An HTM node is abstracted using a coincidence detector and a mixture of Markov chains. In this paper, we describe how Bayesian belief propagation in a spatio-temporal hierarchical model, called Hierarchical Temporal Memory (HTM), can lead to a mathematical model for cortical circuits. The theoretical setting of hierarchical Bayesian inference is gaining acceptance as a framework for understanding cortical computation.
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