Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
What are the computations of the cerebellum, the basal ganglia and the cerebral cortex?
Neural Networks - Special issue on organisation of computation in brain-like systems
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Self-Organizing Maps
Applications of the self-organising map to reinforcement learning
Neural Networks - New developments in self-organizing maps
Bayesian computation in recurrent neural circuits
Neural Computation
Neural Computation
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
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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A number of models based on Bayesian network have recently been proposed and shown to be biologically plausible enough to explain various phenomena in visual cortex. The present work studies how far the same approach can extend to motor learning, in particular, in combination with reinforcement learning, with the aim of suggesting a possible cooperation mechanism of cerebral cortex and basal ganglia. The basis of our model is BESOM, a biologically solid model for cerebral cortex proposed by Ichisugi, but extended with a reinforcement learning capability. We show how reinforcement learning can benefit from Bayesian network computations with unsupervised learning, in particular, in approximate representation of a large state-action space and detection of a goal state. By a simulation with a concrete BESOM network inspired by anatomically known cortical hierarchy to carry out a reach movement task, we demonstrate our model's stable and robust ability for motor learning.