For the 21st century，this study is dedicated to exploring the fundamental theories，models，and architectures of next-generation artificial neural networks （ANNs），with the objective of constructing high-performance ANNs that featuring adaptive topology，interpretability，strong generalization capability，and high energy efficiency. Since the initial proposition of ANNs in the 1940s，the field has witnessed over 80 years of development. Extending to the mid-21st century，ANNs can be categorized into five generations based on five core dimensions，and such generational evolution constitutes the core trajectory of neural network research and advancement. This study defines five generations of ANNs （abbreviated as xG-ANNs） from five perspectives： neuronal unit，information coding，network structure，learning mechanism，and Turing test. 1G-ANNs： Threshold logic networks，represented by the McCulloch-Pitts model and the perceptron； 2G-ANNs： Continuous activation networks （e.g.，sigmoid，tanh），typified by the classical back-propagation network； 3G-ANNs： Represented by spiking neural networks 4G-ANNs： Deep neural networks，represented by AlexNet，ResNet，Transformer architectures，and the attention mechanism，which have passed the conversational Turing test for disembodied intelligence； 5G-ANNs： Cognitive neural networks，with five defining characteristics： 1） cognitive units that integrate memory，reasoning，and human-like attention； 2） hybrid coding of semantic symbols and distributed representations； 3） modular cognitive architecture with dynamic reconfigurable topology； 4） meta-learning，causal reasoning，and lifelong learning； and 5） the embodied Turing test that remains as a breakthrough. A core academic consensus has been formed regarding 5G-ANNs. That is，such networks integrate neural computing，symbolic reasoning，and cognitive architecture with low-power consumption，support dynamic topology，memory cognition，neuro-symbolic fusion，and embodied/world models. They exhibit intrinsic advantages，including adaptive structure，few-shot generalization，interpretability，low energy consumption，and embodied cognition. At present，the field is in the era of 4G-ANNs，which are characterized by data-driven fitting，deep learning，the attention mechanism，and Transformer frameworks. Represented by large language model-based ChatGPT，4G-ANNs have passed the conversational Turing test，but such validation is a “black box” assessment that is restricted to the emergence of disembodied intelligence. The root causes lie in the inherent asymmetry of large language models built on the scaling law of parameter expansion，their lack of comprehension of physical laws that govern the real world，and critical drawbacks，including jagged multimodal and multiform intelligent output and inferior energy efficiency. By contrast，neural networks that are deployed in embodied intelligent robots lack autonomous intelligence and can only execute predefined actions per programmed instructions，leading to a huge gap between disembodied intelligence and embodied intelligence in 4G-ANN systems. Addressing these critical limitations of 4G-ANNs calls for the support of novel theories，models，and architectural designs. At present，extensive debates and divergence persist over the developmental orientation and technical routes of next-generation ANNs. This study analyzes and summarizes the developmental progress of mainstream theories，models，and architectures of the first four generations of ANNs. It focuses on the characteristics of several typical 4G-ANN models and their enhanced variants，and reviews representative architectures for 5G-ANNs，including world models，joint embedding predictive architecture，cognitive spiral models，and intelligence-trace cellular network frameworks. Finally，grounded in theory of cognitive physics and the practical technologies of driving brain cognition，this work proposes a lightweight architecture，called embodied cognitive physics neural network （E-CoPNN），which incorporates the core characteristics of 5G-ANNs. In particular，E-CoPNN features dynamic topology （three-layer nested structure and dual systems），memory cognition （three categories of memory），neuro-symbolic fusion （integration of Euclidean and non-Euclidean data，combination of lexemes and concepts），and embodied cognition/world models （fusion of disembodied and embodied intelligence，integration of cognitive space and physical space）. The proposed architecture embodies the typical properties of 5G-ANNs： an adaptive structure，few-shot generalization，interpretability，low power consumption，and embodied cognition. Targeting brain-like general intelligence，5G-ANNs regard dynamic topology，autonomous memory，cognitive reasoning，and structural evolution as core attributes，shifting the ANN research paradigm from data fitting to structural reconstruction，and thus，representing a pivotal direction for achieving machine autonomous intelligence and continuous learning.Conclusion and Significance： At present，the development of next-generation ANNs for the 21st century will drive a series of transformative revolutions： philosophically，a paradigm shift from mind-body dualism to embodied cognitive science based on embodied perception monism； theoretically，a disciplinary expansion from 20th century biophysics to 21st century cognitive physics； model-wise，a fundamental transition of ANN research from data fitting into structural reconstruction； in application，bridging the long-standing gap between disembodied intelligence and embodied intelligence in ANN development； generationally，an evolutionary leap from 4G-ANNs to 5G-ANNs characterized by brain-like cognitive capabilities and adaptive topological structures. This work lays a solid foundation for the widespread application of embodied cognitive robots with learning，self-development，self-correction，and human-robot interaction capabilities. Furthermore，it supports the convergent development of nano-bio-info-cogno，or the four cutting-edge technologies led by cognitive integration. Finally，this work enhances human intellectual competence and ushers in a new round of cognitive revolution.

artificial neural network （ANN）  /  fifth generation ANN embodied intelligence  /  disembodied intelligence  /  embodied cognition  /  cognitive physics（cognitophysics）  /  embodied cognitive physics neural network （E-CoPNN）  /  unified field theory  /  intelligence field  /  human attention mechanism  /  selective mechanism  /  driving brain cognition  /  review