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Self-replicating artificial neural networks give rise to universal evolutionary dynamics
Boaz Shvartzman1,2, Yoav Ram1,3,4
1School of Zoology, Faculty of Life Sciences, Tel Aviv University; Tel Aviv, Israel.
Plos Computational Biology
|March 28, 2024
Summary
We developed a novel deep-learning model, the self-replicating artificial neural network (SeRANN), which enables endogenous mutation and selection. This model naturally reproduces key evolutionary phenomena, offering new insights into evolutionary dynamics.
Area of Science:
- Computational Biology
- Artificial Intelligence
- Evolutionary Dynamics
Background:
- Traditional evolutionary models often introduce mutations exogenously.
- Understanding endogenous mutation and selection is crucial for biological realism.
Purpose of the Study:
- To introduce a novel deep-learning model, the self-replicating artificial neural network (SeRANN).
- To investigate if endogenous mutation and selection can spontaneously generate evolutionary phenomena.
Main Methods:
- Developed SeRANN, a deep-learning model trained for self-replication (endogenous mutation) and classification (fertility determination).
- Evolved 1,000 SeRANN instances for 6,000 generations.
Main Results:
- Observed emergent evolutionary phenomena including adaptation, clonal interference, and epistasis.
- Demonstrated the evolution of mutation rate and fitness effect distributions.
- Confirmed that implicit, endogenous selection and mutation drive universal evolutionary dynamics.
Conclusions:
- SeRANN successfully models endogenous mutation and selection, leading to emergent evolutionary phenomena.
- The model provides a powerful platform for exploring evolutionary hypotheses and dynamics.
- This approach highlights the potential of AI in simulating complex biological systems.
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