C0nnecti0nist Models and Their Properties

Much of the progress in the fields constituting cognitive science has been based upon the use of explicit information processing models, almost exclusively patterned after conventional serial computers. An extension of these ideas to massively parallel, connectianist models appears to offer a number of advantages. After a preliminary discussion, this paper introduces a general connectionist model and considers how it might be used in cognitive science. Among the issues addressed are: stability and noise-sensitivity, distributed decisionmaking, time and sequence problems, and the representation of complex concepts. Much of the progress in the fields constituting cognitive science has been based upon the use of concrete information processing models (IPM), almost exclusively patterned after conventional sequential computers. There are several reasons for trying to extend IPM to cases where the computations are carried out by a parallel computational engine with perhaps billions of active units. As an introduction, we will attempt to motivate the current interest in massively parallel models from four different perspectives: anatomy, computational complexity, technology, and the role of formal languages in science. It is the last of these which is of primary concern here. We will focus upon a particular formalism, connectionist models (CM), which is based explicitly on an abstraction of our current understanding of the information processing properties of neurons. Animal brains do not compute like a conventional computer. Comparatively slow (millisecond) neural computing elements with complex, parallel connections form a structure which is dramatically different from a high-speed, predominantly serial machine. Much of current research in the neurosciences is concerned with tracing out these connections and with discovering how they transfer information. One purpose of this paper is to suggest how connectionist theories of the brain can be used to produce