Computer-simulated neural networks: an appropriate model for motor development?

The idea of an artificial neural network is introduced in a historical context, and the essential aspect of it, viz., the modifiable synapse, is compared to the aspect of plasticity in the natural nervous system. Based on such an artificial neural network, a model is presented for the way in which (the path along which) the connectivity in the spinal cord is modified during the period that a newborn 'learns' to control the movement of his forearm. In this way an automatic calibration of the receptors and the antagonists' recruitment of motor units is represented. The learning process is described in non-mathematical terminology. The model is then shown to be able after learning to reach target angles outside the training set of angles, and to be able to relearn when an important receptor has been made inoperative. In this way it is shown that the model is able to generalize, and that it is robust against at least some damage.