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 wer e inspired by those of the human arm, We developed the model with a mo tor-learning framework in mind, i.e., with the long-term goal of incor porating it in a parallel distributed learning scheme for the arm cont roller, We then demonstrate the response of the model to various patte rns of activation of the arm muscles in order to study the relative ro le of control strategies and plant properties in trajectory formation, The results of our simulations emphasize the role of the intrinsic pr operties of the plant in generating movements with anthropomorphic qua lities such as smoothness and unimodal velocity profiles, and demonstr ate that the task of an eventual controller for the arm could be simpl y that of programming the amplitudes and durations of steps of neural input without considering additional motor details, Our findings are r elevant to the design of artificial arms and, with some caveats, to th e study of the brain strategies in the arm motor system.