Our current understanding of the primate cerebral cortex (neocortex) and in particular the posterior, sensory association cortex has matured to a point where it is possible to develop a family of graphical models that capture the structure, scale and power of the neocortex for purposes of associative recall, sequence prediction and pattern completion among other functions. Implementing such models using readily available computing clusters is now within the grasp of many labs and would provide scientists with the opportunity to experiment with both hard-wired connection schemes and structure-learning algorithms inspired by animal learning and developmental studies. While neural circuits involving structures external to the neocortex such as the thalamic nuclei are less well understood, the availability of a computational model on which to test hypotheses would likely accelerate our understanding of these circuits. Furthermore, the existence of an agreed-upon cortical substrate would not only facilitate our understanding of the brain but 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.
Content Area: 15. Machine Perception
Subjects: 3.4 Probabilistic Reasoning; 4. Cognitive Modeling
Submitted: May 9, 2005