Generative character of perception: a neural architecture for sensorimotor anticipation
Neural Networks - Special issue on organisation of computation in brain-like systems
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
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We present a biologically motivated computational model that is able to anticipate and evaluate multiple hypothetical sensorimotor sequences. Our Model for Anticipation based on Cortical Representations (MACOR) allows a completely parallel search at the neocortical level using assemblies of rate coded neurons for grouping, separation, and selection of sensorimotor sequences. For a vision-controlled local navigation of a mobile robot Khepera, we can demonstrate that our anticipative approach outperforms a reactive one. We also compare our explicitely planning approach with the implicitely planning Q-learning.