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Evolutionary psychology explores the origins of human behavior and mental processes by framing them within the context of natural selection, a theory famously propounded by Charles Darwin. This field asserts that many behaviors common across human societies — ranging from instinctive fear reactions to complex social interactions — arose as evolutionary adaptations. These adaptations enhanced the survival and reproductive success of our ancestors, thereby becoming embedded in the...
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Biological organization is the classification of biological structures, ranging from atoms at the bottom of the hierarchy to the Earth's biosphere. Each level of the hierarchy represents an increase in complexity that builds upon the previous level.
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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Toward a theory of evolution as multilevel learning.

Vitaly Vanchurin1,2, Yuri I Wolf3, Mikhail I Katsnelson4

  • 1National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894; vitaly.vanchurin@gmail.com koonin@ncbi.nlm.nih.gov.

Proceedings of the National Academy of Sciences of the United States of America
|February 5, 2022
PubMed
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Biological evolution, including the origin of life, is framed as multilevel learning. This theory explains key evolutionary mechanisms like replication and natural selection using learning dynamics and neural networks.

Keywords:
loss functionmajor evolutionary transitionsnatural selectionorigin of lifetheory of learning

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Area of Science:

  • Theoretical Biology
  • Evolutionary Theory
  • Computational Neuroscience

Background:

  • Biological evolution and the origin of life remain complex phenomena.
  • Existing theories may benefit from novel frameworks integrating learning dynamics.

Purpose of the Study:

  • To propose a novel theoretical framework for biological evolution based on the theory of learning.
  • To demonstrate how fundamental evolutionary principles emerge from learning dynamics.
  • To explore the origin of life within this multilevel learning framework.

Main Methods:

  • Application of learning theory to physically renormalizable systems.
  • Formulation of seven fundamental principles of evolution.
  • Utilizing the mathematical framework of neural networks.
  • Analysis of information flow using backpropagation and forward propagation.

Main Results:

  • Seven fundamental principles identified as necessary and sufficient for an observable universe.
  • Replication and natural selection shown to emerge from learning dynamics, particularly loss function minimization.
  • A generalized Central Dogma of molecular biology derived from information flow analysis.
  • The framework provides a basis for analyzing major evolutionary transitions, including the origin of life.

Conclusions:

  • Multilevel learning offers a unifying framework for understanding biological evolution.
  • Neural network mathematics can provide detailed analysis of evolutionary phenomena.
  • The proposed theory offers new perspectives on the origin and progression of life.