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dc.contributor.authorKabdushev, Sherniyaz
dc.contributor.authorGabrielyan, Oleg
dc.contributor.authorKopishev, Eldar
dc.contributor.authorSuleimenov, Ibragim
dc.date.accessioned2026-03-02T05:17:37Z
dc.date.available2026-03-02T05:17:37Z
dc.date.issued2025
dc.identifier.citationKabdushev S, Gabrielyan O, Kopishev E, Suleimenov I. 2025 Neural network properties of hydrophilic polymers as a key for development of the general theory of evolution. R. Soc. Open Sci. 12: 242149. https://doi.org/10.1098/rsos.242149ru
dc.identifier.issn2054-5703
dc.identifier.otherdoi.org/10.1098/rsos.242149
dc.identifier.urihttp://repository.enu.kz/handle/enu/29572
dc.description.abstractThe analysis of the existing literature demonstrates that in order to address the fundamental challenges associated with the origin of life, it is essential to consider this problem from a comprehensive perspective, specifically from the vantage point of the general theory of evolution of complex systems. From these positions, life should be regarded as a distinctive instance of an information storage and processing system that emerges naturally. Evolutionary processes should be examined from the vantage point of the coevolution of material and informational components, which has not been sufficiently emphasized hitherto. It is shown that a specific example in this respect is analogues of neural networks spontaneously formed in solutions of some hydrophilic polymers. Such systems lead to the formation of non-trivial information objects. A wide range of other examples is considered, proving that the processes occurring with the participation of hydrophilic polymers should be interpreted, among other things, from the point of view of formation of information objects, which, under certain conditions, influence the processes occurring at the molecular and supramolecular level. It is shown that it is reasonable to use the tools of classical dialectics to solve such fundamental problems as that of the origin of life.ru
dc.language.isoenru
dc.publisherRoyal society open scienceru
dc.relation.ispartofseries12: 242149;
dc.subjectneural networksru
dc.subjectcoevolutionru
dc.subjectinformation processing systemsru
dc.subjectdialecticsru
dc.subjecthydrophilic polymersru
dc.titleNeural network properties of hydrophilic polymers as a key for development of the general theory of evolutionru
dc.typeArticleru


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