Complexities of modeling the nervous system: limitations of current mathematical and computational approaches
Keywords:
mathematical models, computational models, neural complexity, nervous system, simulationsAbstract
This paper presents an analysis of the limitations of modern mathematical and computational approaches to modeling the nervous system. Although these methods provide meaningful data and form a basis for quantitative description of neurophysiological processes, they often fail to adequately reproduce the complexity of the structural and functional organization of the nervous system. Simplification of neural dynamics and interactions required to implement computational algorithms imposes limitations on the accuracy of models, their predictive ability, and scalability. In addition, the use of complex mathematical models is associated with high computational costs, which hinders their application in real time or for processing large amounts of data. The article considers in detail the main challenges associated with increasing the reliability and biological validity of models and discusses promising directions for the development of computational approaches aimed at integrating more realistic characteristics of the nervous system in the context of modern research.
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