A connectionist model using probability data as weights and parameters

Based on the concept of virtual lateral inhibition [1,2], a two layered connectionist model is developed, and its properties explored. This model is called LIBRA/RX. The flow of acitivation in this model is described by a set of 3N ordinary nonlinear differential equations, where N is the number of nodes on the nets' upper level. The mathematical properties of the equations are explored, and in particular, the dynamics of the net is demonstrated to be convergent in nearly all cases. The model has thus far been employed in the task of pattern recognition, and more recently in control tasks [3]. In the task of pattern recognition, the lower level or input nodes represent the possible features, and the upper level or output nodes represent the possible classes of patterns. This model uses the probabilities of the pattern classes given the features, and the features given the pattern classes as weights. The prior probabilities of the features and pattern classes also appear as parameters—thus, no learning need be involved. Examples of the nets use in classifying patterns are presented and discussed.