神经细胞自动机:适用于生物学和超越经典AI的应用
Benedikt Hartl1, Michael Levin2, Léo Pio-Lopez1
1Allen Discovery Center at Tufts University, Medford, MA, USA.
Physics of life reviews
|December 9, 2025
概括
神经细胞自动机 (NCAs) 使用人工神经网络进行自我组织,建模复杂的生物和人工智能系统. 这些可适应的模型显示强大,目标导向的行为没有中央控制,桥梁生物学和AI.
科学领域:
- 计算生物学 计算生物学
- 人工智能的人工智能
- 生物工程是生物工程.
背景情况:
- 神经细胞自动机 (NCAs) 以可训练的更新规则扩展基于规则的系统,用于建模生物自我组织.
- 国家机关嵌入人工神经网络 (ANN) 来进行本地决策,模拟从分子到系统层面的过程.
- 它们为生物现象如进化,发育和再生提供了多层次的视角.
研究的目的:
- 审查有关国家机关的现有文献,重点关注生物和生物工程应用.
- 突出NCA超越生物学的能力,包括机器人控制和推理任务.
- 将国家国家机关定位为一个统一的范式,将多尺度生物学和生成人工智能联系起来.
主要方法:
- 在细胞自动机框架内使用ANN作为局部代理.
- 实现适应动态的可分化或可演变的更新规则.
- 审查和综合有关NCA应用的现有研究.
主要成果:
- 国家核能机构成功地复制生物模式,并将其推广到新的条件中,显示出对干扰的稳定性.
- 国家核准机构在机器人和人工智能推理等非生物系统中展示了目标导向的动态和适应性.
- 在NCAs中,代状态的改进与现代的生成AI模型类似,例如扩散模型.
结论:
- 国家认证机构为理解和模拟复杂系统提供了一个计算精简,统一的框架.
- 它们将来自多尺度生物学的见解与生成AI的进步相结合.
- 国家机关有潜力设计具有层次推理和控制能力的生物启发的集体智能.
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