Abstract

Motor learning and motor control have been the focus of intense study
by researchers from various disciplines. The neural network model approach has
been very successful in providing theoretical frameworks on motor learning and
motor control by modeling neural and psychophysical data from multiple levels of
biological complexity. Two neural network models of voluntary single-joint movement
organization under normal conditions are summarized here. The models seek
to explain detailed electromyographic data of rapid single-joint arm movement and
identify their neural substrates. The models are successful in predicting several characteristics
of voluntary movement.