自复制的人工神经网络产生了普遍的进化动态
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
概括
我们开发了一种新的深度学习模型,即自我复制的人工神经网络 (SeRANN),可以实现内源突变和选择. 这个模型自然地复制了关键的进化现象,为进化动态提供了新的见解.
科学领域:
- 计算生物学 计算生物学
- 人工智能的人工智能
- 进化的动力学.
背景情况:
- 传统的进化模型往往以外源性方式引入突变.
- 了解内源突变和选择对于生物现实主义至关重要.
研究的目的:
- 引入一种新的深度学习模型,即自我复制的人工神经网络 (SeRANN).
- 调查内生突变和选择是否能自发地产生进化现象.
主要方法:
- 开发了SeRANN,这是一个训练有素的深度学习模型,用于自我复制 (内生突变) 和分类 (生育能力确定).
- 在6000代的时间里演化了1000个SeRANN实例.
主要成果:
- 观察到新兴的进化现象,包括适应,克隆干扰和表现.
- 证明了突变率和健康效应分布的演变.
- 证实了隐性,内生选择和突变驱动了普遍的进化动态.
结论:
- 塞兰成功地模拟了内源性突变和选择,导致了新兴的进化现象.
- 该模型为探索进化假设和动态提供了一个强大的平台.
- 这种方法凸显了人工智能在模拟复杂生物系统方面的潜力.
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