Computational model of the cerebral cortex that performs sparse coding using a Bayesian network and self-organizing maps

Abstract

The authors have proposed a computational model of the cerebral cortex, called the BESOM model, that combines a Bayesian network and Self-Organizing Maps. In this paper, we add another model of the cerebral cortex, called sparse coding, into our model in a biologically plausible way. In the BESOM model, hyper-columns in the cerebral cortex are interpreted as random variables in a Bayesian network. We extend our model so that random variables can become "inactive." In addition, we apply bias at the time of recognition so that almost all of the random variables may become inactive. This mechanism realizes sparse coding without breaking the theoretical framework of the model based on the Bayesian networks. © 2010 Springer-Verlag.