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Computational neuroscience involves the use of mathematical and computational methods to understand how neural systems process information, learn and adapt, and give rise to cognition and behavior. It brings together concepts from computer science, engineering, mathematics, physics, statistics, and neuroscience to model and simulate brain function at multiple scales, from individual neurons to large-scale networks. Research in this field aims to understand the principles underlying neural coding, computation, and plasticity, as well as to develop new theories, models, algorithms, and technologies for studying and manipulating neural systems. By elucidating the computational mechanisms of the brain, computational neuroscience contributes to our understanding of the normal and pathological functioning of the nervous system, and has potential applications in medicine, education, entertainment, and artificial intelligence. As a subfield of neuroscience, it shares the broader goals of advancing knowledge about the structure, function, development, and disorders of the nervous system, but focuses on the quantitative and theoretical aspects of neural data analysis, modeling, and prediction.