| Summary: | High performance computing (HPC) is transforming the field of large - scale brain simulation by enabling the integration of multi - scale computational modeling with massive neuroscience data. With advanced HPC resources, researchers can simulate neural activities from ion-channel dynamics to whole - brain network interactions, thereby illuminating the mechanisms underlying cognition, neural disorders, and emerging neuromorphic intelligence. This review examines the theoretical principles and technical foundations of supercomputer brain simulation, including distributed parallel algorithms, graphics processing unit (GPU) - based acceleration, and multimodal data management. It also surveys prominent simulation platforms such as NEST, NEURON, and The Virtual Brain (TVB), highlighting their strengths in modeling spiking neuronal network (SNN), multicompartmental neurons, and large - scale functional connectivity, respectively. Furthermore, we discuss the practical applications of these simulations in elucidating disease mechanisms in Alzheimer's disease (AD), Parkinson's disease (PD), autism spectrum disorder (ASD), schizophrenia, and epilepsy. Special emphasis is placed on how supercomputer brain simulation assists in virtual drug screening, optimizing deep brain stimulation parameters, and supporting digital twin approaches for personalized medicine. Finally, we address the critical challenges and future directions in this rapidly evolving domain, including the trade-off between computational cost and biological realism, data integration and validation, and the necessity for interdisciplinary collaboration. The advent of exascale supercomputers and the convergence of neuroinformatics and machine learning (ML) are poised to propel brain simulation research toward unprecedented clinical and scientific breakthroughs.
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