This text presents neural network theory for diverse applications in a unified way, where the structural of artificial neural networks are characterized by distinguished classes of graphs. The book proceeds from a clear but concise exposition of neuroscience fundamentals, graph theory and alogorithms to a detailed analysis of perceptron and lms-theory based neural networks, multilayer feedforward networks, and self-organizing and competitive learning neural networks. The text culminates with a chapter on selected applications.