Adaptive vector integration to endpoint: Self-organizing neural circuits for control of planned movement trajectories

Author(s): Gaudiano, P. | Grossberg, S. |

Year: 1992

Citation: Human Movement Science, 11, 141-155

Abstract: A neural network model is described for adaptive control of arm movement trajectories during visually guided reaching. The model clarifies how a child, or infant robot, can learn to reach for objects that it sees. Piaget has provided basinc insights with his concept of a circular reaction. As an infant makes internally generated hand movements, the eyes automatically follow it's motion. A transformation is learned between the visual representation of hand position and the motor representation of hand position. Learning of this transformation eventually enables the child to accurately reach fo visually detected targets. Grossberg and Kuperstein (1989) have shown how the eye movement of system can use visulla error signals to cerrect movement parameters via cerebellar learning. Here it is shown how the arm movement system can endogenously generate movement which lead to adaptive tuning of arm control parameters. These movements also activate representations that are used to learn the visuo-motor transformation that controls visually guided reaching. The arm movement poperties obtain in the Adaptive Vector Integration to Endpoint (AVITE) model an adaptive neural circuit based on the VITE model for arm and speech trajectory generation of Bullock and Grossberg (1998a).

Topics: Biological Learning, Biological Vision, Models: Other,

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