Abstract
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.
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Ichisugi, Y. (2012). A Computational Model of Motor Areas Based on Bayesian Networks and Most Probable Explanations. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33269-2_91
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DOI: https://doi.org/10.1007/978-3-642-33269-2_91
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