A neural network architecture for cue-based motion planning

Abstract

The principles of memory organization of plans are presented, and the role of sensory cues in the timely selection and execution of plans is demonstrated. The two major components of learning a cue-based plan, developing the ability to detect cues and associating cues with the relevant responses, are described. The preliminary development of neural-network mechanisms for learning cue-based plans is presented. It is shown that hard-wired neural networks provide the input to adaptive neural networks that learn an internal representation of the relevant cues and the threshold levels associated with them. Self-organizing neural networks learn to associate cues with changes in action and to construct cue-based plans.<>