The role of plant properties in arm trajectory formation: a neural network study

In this paper, we first introduce a neural network model of a planar, six-muscle, redundant arm whose structure and operation principles were inspired by those of the human arm. We developed the model with a motor-learning framework in mind, i.e., with the long-term goal of incorporating it in a parallel distributed learning scheme for the arm controller. We then demonstrate the response of the model to various patterns of activation of the arm muscles in order to study the relative role of control strategies and plant properties in trajectory formation. The results of our simulations emphasize the role of the intrinsic properties of the plant in generating movements with anthropomorphic qualities such as smoothness and unimodal velocity profiles, and demonstrate that the task of an eventual controller for the arm could be simply that of programming the amplitudes and durations of steps of neural input without considering additional motor details. Our findings are relevant to the design of artificial arms and, with some caveats, to the study of the brain strategies in the arm motor system.