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引用次数: 0
摘要
由于微观层面的观测数据无法直接捕捉到突发行为,因此以数据驱动的方式量化复杂动态系统的突发现象并建立突发动态模型具有挑战性。因此,开发一个框架,利用现有数据在宏观层面识别突发现象和捕捉突发动力学至关重要。受因果涌现(CE)理论的启发,本文介绍了一种机器学习框架,用于学习涌现潜空间中的宏观动态,并量化因果涌现的程度。该框架最大限度地增加了有效信息,从而产生了一个具有更强因果效应的宏观动力学模型。模拟数据和真实数据的实验结果证明了所提框架的有效性。它能在各种条件下有效量化 CE 度,并揭示不同噪声类型的独特影响。它可以从 fMRI 数据中学习一维粗粒度宏观状态,以表示观看电影片段时的复杂神经活动。此外,在所有模拟数据中,都能观察到对不同测试环境的改进泛化。
Finding emergence in data by maximizing effective information
Quantifying emergence and modeling emergent dynamics in a data-driven manner for complex dynamical systems is challenging due to emergent behaviors cannot be directly captured by micro-level observational data. Thus, it’s crucial to develop a framework to identify emergent phenomena and capture emergent dynamics at the macro-level using available data. Inspired by the theory of causal emergence (CE), this paper introduces a machine learning framework to learn macro-dynamics in an emergent latent space and quantify the degree of CE. The framework maximizes effective information, resulting in a macro-dynamics model with enhanced causal effects. Experimental results on simulated and real data demonstrate the effectiveness of the proposed framework. It quantifies degrees of CE effectively under various conditions and reveals distinct influences of different noise types. It can learn a one-dimensional coarse-grained macro-state from fMRI data, to represent complex neural activities during movie clip viewing. Furthermore, improved generalization to different test environments is observed across all simulation data.
期刊介绍:
National Science Review (NSR; ISSN abbreviation: Natl. Sci. Rev.) is an English-language peer-reviewed multidisciplinary open-access scientific journal published by Oxford University Press under the auspices of the Chinese Academy of Sciences.According to Journal Citation Reports, its 2021 impact factor was 23.178.
National Science Review publishes both review articles and perspectives as well as original research in the form of brief communications and research articles.