A neocortically-derived model of continuous contextual processing

The architectural regularities shared among most neocortical regions suggest repeated functional units that confer core computational capabilities to otherwise very different cortical areas. This paper addresses the massive cortico-cortical feedforward-feedback system connecting most adjacent cortical areas and discusses a computational model derived from these feedforward-feedback loops. First results obtained using a partial implementation of the model show context-dependent pattern recognition capabilities such as generalization, noise tolerance, pattern completion, and cued associative recall, even with unsegmented input data