Sigma-Pi learning: on radial basis functions and cortical associative learning
Advances in neural information processing systems 2
Vector quantization and signal compression
Vector quantization and signal compression
Self-organizing maps
Complex behavior by means of dynamical systems for an anthropomorphic robot
Neural Networks - Special issue on organisation of computation in brain-like systems
Reinforcement Learning
Neuro-Dynamic Programming
Self-organizing continuous attractor networks and motor function
Neural Networks
Task planning under uncertainty using a spreading activation network
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Coordinating with the Future: The Anticipatory Nature of Representation
Minds and Machines
Self-Organizing Sensorimotor Maps Plus Internal Motivations Yield Animal-Like Behavior
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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Experimental studies of reasoning and planned behavior have provided evidence that nervous systems use internal models to perform predictive motor control, imagery, inference, and planning. Classical (model-free) reinforcement learning approaches omit such a model; standard sensorimotor models account for forward and backward functions of sensorimotor dependencies but do not provide a proper neural representation on which to realize planning. We propose a sensorimotor map to represent such an internal model. The map learns a state representation similar to self-organizing maps but is inherently coupled to sensor and motor signals. Motor activations modulate the lateral connection strengths and thereby induce anticipatory shifts of the activity peak on the sensorimotor map. This mechanism encodes a model of the change of stimuli depending on the current motor activities. The activation dynamics on the map are derived from neural field models. An additional dynamic process on the sensorimotor map (derived from dynamic programming) realizes planning and emits corresponding goal-directed motor sequences, for instance, to navigate through a maze.