通过最大限度地利用有效的信息,发现数据中的出现
Mingzhe Yang1, Zhipeng Wang1, Kaiwei Liu1
1School of Systems Science, Beijing Normal University, Beijing 100875, China.
National science review
|January 6, 2025
概括
本研究介绍了一种机器学习框架,通过识别新出现的现象来建模复杂系统动态. 该方法量化因果出现 (CE),并在宏动力学模型中增强因果效应.
科学领域:
- 复杂系统科学 复杂系统科学
- 机器学习 机器学习
- 动态系统理论 动态系统理论
背景情况:
- 在复杂系统中建模新出现的行为是很困难的,因为它们不能从微观数据直接观察到.
- 现有的方法很难从可用的观测数据中捕捉宏观层面的动态.
- 因果出现 (CE) 理论为理解宏观层次因果关系提供了一个框架.
研究的目的:
- 开发一个数据驱动的机器学习框架,用于识别新出现的现象.
- 在复杂的动态系统中量化因果出现 (CE) 的程度.
- 在一个新兴的潜空间中学习宏观动力学.
主要方法:
- 开发了一种机器学习框架,灵感来源于因果出现 (CE) 理论.
- 该框架通过在新出现的潜在空间中最大化有效信息来学习宏观动力学.
- 将框架应用于模拟和现实世界的功能磁共振成像 (fMRI) 数据.
主要成果:
- 该框架有效量化了各种条件下的因果出现 (CE) 的程度.
- 不同类型的噪声对CE量化有着不同的影响.
- 从fMRI数据中学习了一个代表神经活动的单维宏观状态.
- 在模拟数据中观察到在不同测试环境中改进的概括性.
结论:
- 拟议的机器学习框架成功地建模了宏观动力学,并量化了因果出现 (CE).
- 这种方法提供了一个强大的方法来分析复杂的系统及其新出现的特性.
- 在模拟数据和真实世界神经成像数据上都表现出有效性.
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