A Computational Model of the Cerebral Cortex
Venue
Proceedings of AAAI-05, MIT Press, Cambridge, Massachusetts (2005), pp. 938-943
Publication Year
2005
Authors
BibTeX
Abstract
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.
