Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Self-Organizing Maps
Neural Networks
Cortical circuitry implementing graphical models
Neural Computation
A motor learning neural model based on Bayesian network and reinforcement learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
The recommendation architecture: lessons from large-scale electronic systems applied to cognition
Cognitive Systems Research
A computational model of motor areas based on bayesian networks and most probable explanations
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
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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.