John Shawe-Taylor, Jieyu Zhao
We propose a way of using boolean circuits to perform real valued computation in a way that naturally extends their boolean func(cid:173) tionality. The functionality of multiple fan in threshold gates in this model is shown to mimic that of a hardware implementation of continuous Neural Networks. A Vapnik-Chervonenkis dimension and sample size analysis for the systems is performed giving best known sample sizes for a real valued Neural Network. Experimen(cid:173) tal results confirm the conclusion that the sample sizes required for the networks are significantly smaller than for sigmoidal networks.