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• W4210365025 abstract "We apply the theory of learning to physically renormalizable systems in an attempt to outline a theory of biological evolution, including the origin of life, as multilevel learning. We formulate seven fundamental principles of evolution that appear to be necessary and sufficient to render a universe observable and show that they entail the major features of biological evolution, including replication and natural selection. It is shown that these cornerstone phenomena of biology emerge from the fundamental features of learning dynamics such as the existence of a loss function, which is minimized during learning. We then sketch the theory of evolution using the mathematical framework of neural networks, which provides for detailed analysis of evolutionary phenomena. To demonstrate the potential of the proposed theoretical framework, we derive a generalized version of the Central Dogma of molecular biology by analyzing the flow of information during learning (back propagation) and predicting (forward propagation) the environment by evolving organisms. The more complex evolutionary phenomena, such as major transitions in evolution (in particular, the origin of life), have to be analyzed in the thermodynamic limit, which is described in detail in the paper by Vanchurin et al. [V. Vanchurin, Y. I. Wolf, E. V. Koonin, M. I. Katsnelson, Proc. Natl. Acad. Sci. U.S.A. 119, 10.1073/pnas.2120042119 (2022)]." @default.
• W4210365025 created "2022-02-08" @default.
• W4210365025 creator A5012792532 @default.
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• W4210365025 date "2022-02-04" @default.
• W4210365025 modified "2023-10-18" @default.
• W4210365025 title "Toward a theory of evolution as multilevel learning" @default.
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• W4210365025 doi "https://doi.org/10.1073/pnas.2120037119" @default.
• W4210365025 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35121666" @default.
• W4210365025 hasPublicationYear "2022" @default.
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