The book "Multi-Valued and Universal Binary Neurons" provides a comprehensive examination of two novel types of neurons: multi-valued neurons and universal binary neurons. These neurons are based on the arithmetic of complex numbers, offering enhanced capabilities compared to conventional neurons in artificial neural networks. The networks presented are capable of not only realizing threshold input/output maps but also implementing arbitrary Boolean functions. The book is divided into three main parts: a mathematical analysis of the unique properties of these neurons, learning methods for networks with these neurons, and the practical applications of these neural networks. The case studies presented demonstrate the broad applicability in various fields such as image processing, pattern recognition, and prediction. This extensive treatment of this important class of neural networks is the first of its kind in Western literature and is aimed at anyone interested in the theory, applications, and learning of neural networks.