This paper presents a comprehensive structural and functional analysis of modern neural network architectures, including Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, and state-space models. It introduces a unified analytical framework based on three key dimensions: structural properties (tensor organization), semantic properties (inductive biases), and dynamic properties (gradient flow and optimization behavior). This framework enables a deeper understanding of how architectures process information, construct representations, and differ in computational efficiency. The study analyzes information flow patterns, representation geometry, and computational trade-offs across architectures, while also investigating compositional constraints that explain why certain hybrid models succeed and others fail. By synthesizing theoretical and empirical insights, the paper presents neural architecture evolution as a sequence of solutions to fundamental information processing challenges, and provides design principles for building efficient and scalable deep learning systems.
Khan et al. (Sat,) studied this question.