Title of the article

4.Network Analysis

The distributed representations in Neural Networks prevent the analysis of generalizations in trained models by simple observation, which symbolic learning methods allow. Smaller NNs may be analyzed to some extent by examination, but for larger networks this is practically impossible.

It is possible, however, to analyze trained networks to extract abstract knowledge about their behavior. Elman (1988), for example, trained an SRN to learn sentences and then analyzed the hidden layer activations of that SRN in various contexts, from which he showed that the network had internally developed syntactical categories. Similarly, we trained SRNs on phonotactics (Stoianov et al., 1998), and then analyzed the network statically, by viewing the weight vectors of each neuron as pattern classifiers. We showed that the SRN had induced generalizations about phonetic categories. We follow that earlier work in order to study network behavior, and we present the results of this study in the first subsection.

Another approach to the analysis of connectionist models assumes that they are black boxes and examines the variation of network performance while varying some properties of the data (Plaut et al., 1996; Stoianov, Stowe & Nerbonne, 1999). For example, one can vary word frequency, length, etc., and study the network error. When modeling human cognitive functions with this approach one can compare the behavior of the cognitive system and its artificial models. For example, in phonotactic modeling, one can compare results from psycholinguistic studies of a lexical decision task with the network reaction. This will be subject of study in the rest of the section.