Speed-accuracy trade-off of arm movement predicted by the cascade neural network model

The cascade neural network as a model of the brain motor control of multijoint arms was studied. A computer simulation of the model as implemented and its trajectory-formation properties were examined. It was found that the cascade model can calculate the exact minimum torque-change trajectory only when the penalty method is used (i.e. the electrical conductance of the gap junction in the model is gradually decreased to zero) and the number of relaxation iterations is sufficiently large. On the other hand, when the electrical conductance is fixed and the number of iterations is rather small, the cascade model cannot calculate the exact torque, and the hand does not reach the desired target using feedforward control alone. Thus, an error between the final position and the desired target location was observed. This turned out to be not the weak point of the cascade model, but rather its virtue; the cascade model reproduced the planning-time-accuracy tradeoff, and speed-accuracy tradeoff of the arm movement, known as Fitt´s law