|Our general approach pertains to a scalable, hierarchical Bayesian-inference deep learning architecture which comprises of a basic cortical circuit that populates the homogeneously hierarchy. Representation of high-dimensional patterns is achieved in a manner driven by regularities in the observations, such that complex features are formed across the hierarchy. Each node in our architecture makes sense of current belief information from the layer above it, in conjunction with the observation seen below. Imperfect observations are recoverable through an online unsupervised learning engine. The results reflect robust pattern recognition capabilities suitable to a wide range of applications.
For a reference on deep learning architectures please see: I. Arel, D. Rose, T. Karnowski, "Deep Machine Learning - A New Frontier in Artificial Intelligence Research," IEEE Computational Intelligence Magazine, Vol. 14, pp. 12-18, November, 2010 [pdf]