NEURONAL REPRESENTATION OF BODY MOBILITY FOR GENERATION OF SPONTANEOUS REACHING MOVEMENT

Human motions seem to be so generated as not to oppose body mobility resistance due to anatomical constraints such as limb kinematics, inertial properties of limbs, range of joint motion, and muscle size and attachment. This implies that the human nervous system (the brain) may somehow represent the anatomical constraints and that neuronal representation of body mobility may be utilized so as to spontaneously generate a natural reaching motion. In this study, the body mobility due to anatomical constraints was hypothesized to be represented by a series of self-organizing topographic maps, and we attempted to develop a neural network model that can spontaneously generate natural reaching motions based on those maps. The musculo-skeletal system of the human upper extremity is constructed as three rigid, two-dimensional links in a sagittal plane. A visco-elastic element is attached around each joint to represent passive joint structure. A total of eight muscles are attached around the joints, generating force according to muscle activation signals from the nervous system. The nervous system is modeled as an artificial neural network that incorporates topographically arranged arrays of neurons (topographic maps), the weights of connections of which represent negative gradient vectors of potential functions defining the body mobility due to anatomical constraints and task constraints of body motion. Thus the nervous system may autonomously generate muscular activation signals that tend to move the hand to a goal, while yielding to the mobility of the musculo-skeletal system, if the neuronal representation of body mobility is correctly organized. In this study, a biologically motivated self-organizing mapping algorithm was constructed in order to acquire the maps. As a result of the proposed learning algorithm, the maps are successfully self-organized from a series of random movements of the arm. The motion generated using the maps actually improves as learning proceeds. The simulation results imply that these neuronal maps representing body mobility due to anatomical constraints are probably obtained in the brain for generation of natural motions, and these maps can be self-organized by a relatively simple algorithm. The existence of these neuronal mechanisms seems to be indispensable for spontaneous motion generation, indicating that the proposed mechanisms may be incorporated in actual human motor control.