Biologically-Inspired Learning

Abstract

A general overview of biologically-inspired learning in the paradigm of artificial neural systems is described. In order to have the reader become familiar with fundamentals underlying this paradigm, a substantial and concise hierarchical background from neurophysiology to neuro-computational models is provided with as much clarity as possible. As an application of a well-known artificial neural network algorithm called feed-forward multi-layer perceptron with back-propagation training algorithm is utilized in implementation of an artificial olfactory system also called electronic nose. For improved classification performance, an algorithm as a preprocessing called linear-discriminant analysis is adapted to chosen neural architecture. The main purpose of the preprocessing stage is to lend better scattered input patterns for classes in the feature space compared to that without preprocessing. The performance improvement is also investigated in terms convergence rate, i.e. the number of iteration, given a number of layers, and recalling or generalization capability of the classifier.