ArticleProceedings of the National Academy of Sciences of the United States of America2022
Toward a theory of evolution as multilevel learning.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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42 citing papers in PubMed.
- The Emergence of Life in the Light of Evolution.ArXiv · 2026Article
- Advances in cardiac devices and bioelectronics augmented with artificial intelligence.The Journal of physiology · 2026Review
- Geometric Learning Dynamics.Biological cybernetics · 2026Article
- The selfish ribosome.PLoS biology · 2026Article
- Heuristically Adaptive Diffusion-Model Evolutionary Strategy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- The selfish ribosome.ArXiv · 2026Article
- Polyacid Solutions as an Analogue of a Neural Network.Polymers · 2026Article
- The key role of cheaters in the persistence of cooperation.BMC biology · 2026Article
- The phantastic organ: predictive processing in the brain.Frontiers in psychology · 2026Review
- Distributionally robust free energy principle for decision-making.Nature communications · 2025Article
- Bifurcations and phase transitions in the origins of life.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Review
- Generalizing the central dogma as a cross-hierarchical principle of biology.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Review
- Dataset-Learning Duality and Emergent Criticality.Entropy (Basel, Switzerland) · 2025Article
- Implications From the Analogous Relationship Between Evolutionary and Learning Processes.BioEssays : news and reviews in molecular, cellular and developmental biology · 2025Review
- A Unified Explanation for Drug Repurposing and Pharmacological Pleiotropy Based on Classical and Statistical Thermodynamics.Pharmacology research & perspectives · 2025Review
- Article
- Neural network properties of hydrophilic polymers as a key for development of the general theory of evolution.Royal Society open science · 2025Review
- Eigenmodes of the deep unconscious: the neuropsychology of Jungian archetypes and psychedelic experience.Neuroscience of consciousness · 2025Review
- Exploring the complex information processes underlying plant behavior.Plant signaling & behavior · 2024Review
- Control of Overfitting with Physics.Entropy (Basel, Switzerland) · 2024Article
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4 authors.
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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,
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