A computational model of motor areas based on bayesian networks and most probable explanations

Authors:
Yuuji Ichisugi
Affiliations:
National Institute of Advanced Industrial Science and Technology(AIST), Tsukuba, Ibaraki, Japan
Venue:
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Year:
2012

Citing 8
Cited 0

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
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
Visual shape recognition neural network using besom model

ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Bayesian interpretation of border-ownership signals in early visual cortex

ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Computational model of the cerebral cortex that performs sparse coding using a Bayesian network and self-organizing maps

ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Multinomial bayesian learning for modeling classical and nonclassical receptive field properties

Neural Computation

Quantified Score

Hi-index	0.00

Visualization

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.