Proceedings of the Annual Meeting of the Cognitive Science Society

The recent development of powerful learning algorithms for parallel distributed networks has made it possible to program computation in a new way. These new techniques allow us to program massively parallel networks by example rather than by algorithm. This kind of extensional programming is especially useful when there are no known techniques for solving a problem. This is often the case with the computations associated with basic cognitive processes such as vision and audition. In this paper w e apply the technique to the problem of learning an efficient internal representation of image information direcdy from a gray-scale image. W e compare the results of this to the engineering version of this problem, i.e., image compression. Our results demonstrate that a very simple learning method learns internal representations that are nearly as efficient as those developed by the best known techniques in image compression. Thus w e have a technique whereby neuron-like networks can self-organize to form a compact representation of a visual environment.