The book "Complex-Valued Neural Networks with Multi-Valued Neurons" by Igor Aizenberg offers a comprehensive examination of the theory and applications of Multi-Valued Neurons (MVN). MVNs are complex-valued neurons whose inputs and outputs lie on the unit circle. They are characterized by an activation function that depends solely on the phase of the weighted sum. These neurons enable an effective learning method based on a corrective rule that does not require derivatives. The book discusses the ability of MVNs to learn non-linearly separable problems, such as the XOR problem. Additionally, the application of MVNs in multilayer neural networks (MLMVN) is explained, which exhibit superior learning speed and generalization capability. The concepts and techniques presented are significant for both theoretical and practical applications in the field of machine learning.