Density Plots of Hidden Value Unit Activations Reveal Interpretable Bands

A particular backpropagation network, called a network of value units, was trained to detect problem type and validity of a set of logic problems. This network differs from standard networks in using a Gaussian activation function. After training was successfully completed, jittered density plots were computed for each hidden unit, and used to represent the distribution of activations produced in each hidden unit by the entire training set. The density plots revealed a marked banding. Further analysis revealed that almost all of these bands could be assigned featural interpretations, and played an important role in explaining how the network classified input patterns. These results are discussed in the context of other techniques for analyzing network structure, and in the context of other parallel distributed processing architectures.