Abstract
The neurosemantic approach allows the formation of information management systems based on incoming information flows. This paper explores the possibilities of molecular genetic algorithms of multiscale information analysis for displaying neurosemantic networks in various parametric spaces. The practical application of such a combined approach for tasks of molecular diagnostics of genetic diseases using Walsh’s orthogonal functions is proposed. Issues of interaction between semantics and semiotics in the context of the phenomenon of consciousness and brain are considered. A kinetic approach is also used to construct random graphs, which in a geometric version allows obtaining complex structures; after a percolation transition, a complex cluster can be compared with elements of consciousness. The importance of the study is due to the fact that scientific progress requires new methods to optimize the perception of the results of neural network analysis, as well as the perception of big data with the possibility of analysis at various scales of visualization. Also, of interest to science are model experiments in the field of the theory of the origin of consciousness.
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Stepanyan, I.V., Lednev, M.Y., Aristov, V.V. (2021). Genetic Interpretation of Neurosemantics and Kinetic Approach for Studying Complex Nets: Theory and Experiments. In: Hu, Z., Petoukhov, S., He, M. (eds) Advances in Artificial Systems for Medicine and Education IV. AIMEE 2020. Advances in Intelligent Systems and Computing, vol 1315. Springer, Cham. https://doi.org/10.1007/978-3-030-67133-4_2
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