Optimal Unsupervised Motor Learning Predicts the Internal Representation of Barn Owl Head Movements

(Masino and Knudsen 1990) showed some remarkable results which suggest that head motion in the barn owl is controlled by distinct circuits coding for the horizontal and vertical components of movement. This implies the existence of a set of orthogonal internal coordinates that are related to meaningful coordinates of the external world. No coherent computational theory has yet been proposed to explain this finding. I have proposed a simple model which provides a framework for a theory of low-level motor learning. I show that the theory predicts the observed microstimulation results in the barn owl. The model rests on the concept of "Optimal U nsupervised Motor Learning", which provides a set of criteria that predict optimal internal representations. I describe two iterative Neural Network algorithms which find the optimal solution and demonstrate possible mechanisms for the development of internal representations in animals.