Schema generation in recurrent neural nets for intercepting a moving target

• Published in: Biological cybernetics Vol. 102; no. 6; pp. 451 - 473
• Main Author: Fleischer, Andreas G
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Berlin/Heidelberg : Springer-Verlag 01.06.2010; Springer-Verlag; Springer Nature B.V
• Subjects: Arms; Bioinformatics; Biomechanical Phenomena; Biomechanics; Biomedical and Life Sciences; Biomedicine; Complex Systems; Computer Appl. in Life Sciences; Cybernetics; Eye Movements; Eye-hand coordination; Feedforward controller; Hand; Humans; Interception; Interception of a moving target; Movement; Neural field theory; Neural networks; Neural Networks (Computer); Neurobiology; Neurosciences; Original Paper; Population coding; Recurrent neural nets; Schema theory; Interception of a moving target; Recurrent neural nets; Feedforward controller; Neural field theory; Population coding; Schema theory; Eye-hand coordination
• ISSN: 0340-1200; 1432-0770; 1432-0770
• DOI: 10.1007/s00422-010-0378-6

The grasping of a moving object requires the development of a motor strategy to anticipate the trajectory of the target and to compute an optimal course of interception. During the performance of perception-action cycles, a preprogrammed prototypical movement trajectory, a motor schema, may highly reduce the control load. Subjects were asked to hit a target that was moving along a circular path by means of a cursor. Randomized initial target positions and velocities were detected in the periphery of the eyes, resulting in a saccade toward the target. Even when the target disappeared, the eyes followed the target's anticipated course. The Gestalt of the trajectories was dependent on target velocity. The prediction capability of the motor schema was investigated by varying the visibility range of cursor and target. Motor schemata were determined to be of limited precision, and therefore visual feedback was continuously required to intercept the moving target. To intercept a target, the motor schema caused the hand to aim ahead and to adapt to the target trajectory. The control of cursor velocity determined the point of interception. From a modeling point of view, a neural network was developed that allowed the implementation of a motor schema interacting with feedback control in an iterative manner. The neural net of the Wilson type consists of an excitation-diffusion layer allowing the generation of a moving bubble. This activation bubble runs down an eye-centered motor schema and causes a planar arm model to move toward the target. A bubble provides local integration and straightening of the trajectory during repetitive moves. The schema adapts to task demands by learning and serves as forward controller. On the basis of these model considerations the principal problem of embedding motor schemata in generalized control strategies is discussed.