Theoretical accounts of the primate cerebral cortex (neocortex) are sufficiently rich in their predictive power and detailed in their specification that they warrant a concerted effort to implement and subject to computational experiment. This project applies recent work in theoretical neuroscience to develop statistical models and related learning and inference algorithms that capture the structure, scale and power of the neocortex for applications requiring robust associative recall, sensor fusion, pattern completion and sequence prediction. The cortical models and algorithms are implemented on moderate-sized computing clusters by distributing the computations among a large number of weakly coupled processes, each of which is capable of reproducing the aggregate behavior of a columnar cortical structure consisting of several thousand neurons. These simulated cortical columns are organized hierarchically much as they are in the primate neocortex. In addition to providing insight into the structure and function of the neocortex, the resulting algorithms and statistical models will enable researchers to combine lessons learned from biology with state-of-the-art graphical-model and machine-learning techniques to design hybrid systems that combine the best of biological and traditional computing approaches.
