Computer Sciences Commons^™

Digital Neural Networks, Tony R. Martinez

Faculty Publications

Demands for applications requiring massive parallelism in symbolic environments have given rebirth to research in models labeled as neural networks. These models are made up of many simple nodes which are highly interconnected such that computation takes place as data flows amongst the nodes of the network. To present, most models have proposed nodes based on simple analog functions, where inputs are multiplied by weights and summed, the total then optionally being transformed by an arbitrary function at the node. Learning in these systems is accomplished by adjusting the weights on the input lines. This paper discusses the use of …