A Framework for Understanding Neural Network Component Interactions and Selection Principles, Guidelines, and Empirical Evidence

Neural network architectures consist of several related components, including activation functions, weight initialization methods, normalization, optimizers, and architectures. Whereas the literature in the area has been comprehensive in describing each component separately, there is little evidence on the critical synergies and incompatibilities arising from their interactions. The paper presents a comprehensive Component Interaction Framework (CIF) that visualizes the connections among basic neural network building blocks and provides guidelines for their effective combination. We examine the effects of activation functions on weight initialization conditions, the effect of normalization strategies on optimizer choice, and how optimizer choices are affected by the architectural pattern, such as residual connections, to improve compatibility of components. A systematic analysis and experimental confirmation show that the correct pairing of different components can achieve 2-3 times faster convergence than incompatible pairs. Our system will provide novice and advanced researchers with practical decision trees and compatibility matrices to construct effective neural architectures. This publication bridges the gap between theoretical knowledge and practical architectural engineering and provides a coherent view of neural network design that focuses not on optimizing components in isolation but on overall reasoning.