Comparison of neural networks and an optical thin-film multilayer model for connectionist learning

Current work on connectionist models has been focused largely on artificial neural networks that are inspired by the networks of biological neurons in the human brain. However there are also other connectionist architectures that differ significantly from this biological exemplar. Li and Purvis (1999) proposed a connectionist learning architecture inspired by the physics associated with optical coatings of multiple layers of thin-films. The proposed model differs significantly from the widely used neuron-inspired models. With thin-film layer thicknesses serving as adjustable parameters (as compared with connection weights in a neural network) for the learning system, the optical thin-film multilayer model (OTFM) is capable of approximating virtually any kind of highly nonlinear mappings. We focus on a detailed comparison of a typical neural network model and the OTFM. We describe the architecture of the OTFM and show how it can be viewed as a connectionist learning model. We then present the experimental results of using the OTFM in solving a classification problem typical of conventional connectionist architectures