The Cascade Neural Network Model and a Speed-Accuracy Trade-Off of Arm Movement

Abstract

We propose a hybrid neural network model of aimed arm movements that consists of a feedforward controller and a postural controller. The cascade neural network of Kawato, Maeda, Uno, and Suzuki (1990) was employed as a computational implementation of the feedforward controller. This network computes feedforward motor commands based on a minimum torque-change criterion. If the weighting parameter of the smoothness criterion is fixed and the number of relaxation iterations is rather small, the cascade model cannot calculate the exact torque, and the hand does not reach the desired target by using the feedforward control alone. Thus, one observes an error between the final position and the desired target location. By using a fixed weighting parameter value and a limited iteration number to simulate target-directed arm movements, we found that the cascade model generated a planning time–accuracy trade-off, and a quasi–power-law type of speed–accuracy trade-off. The model provides a candidate neural mechanism to explain the stochastic variability of the time course of the feedforward motor command. Our approach also accounts for several invariant features of multijoint arm trajectories, such as roughly straight hand paths and bell-shaped speed profiles.