In this chapter the basic node structure and node training algorithm used in the model are developed. In the current model, the basic node is a Threshold Logic Unit (TLU), or more specifically, a Linear Threshold Unit (LTU). The standard LTU is a thresholded linear equation that is used for the binary categorization of feature patterns. The primary learning process for a node is the perceptron training algorithm. Although neither the representation nor the training strategy is explicitly biological, an LTU is a standard (simplified) model of neural computation, and perceptron training is sufficiently similar to classical conditioning to be of interest. Possible neural mechanisms for perceptron training have been worked out to a considerable extent. Both the node representation and the training algorithm are sufficiently simple to permit a significant amount of analysis. Variations on the basic model are considered and possible extensions to deal with continuous input values are explored.
KeywordsWeight Vector Classical Conditioning Input Pattern Node Structure Binary Feature
Unable to display preview. Download preview PDF.