We describe a computational model of motor areas of the cerebral cortex. The model combines Bayesian networks, competitive learning and reinforcement learning. We found that decision-making using MPE (Most Probable Explanation) approximates the ideal decision-making in this model, which suggests that MPE calculation is a promising model of not only sensory-cortex recognition, already addressed by previous works, but also motor-cortex decision-making.
['Yuuji Ichisugi']
A computational model of motor areas based on bayesian networks and most probable explanations