Information decomposition in complex systems via machine learning.

ABSTRACT: One of the fundamental steps toward understanding a complex system is identifying variation at the scale of the system's components that is most relevant to behavior on a macroscopic scale. Mutual information provides a natural means of linking variation across scales of a system due to its independence of functional relationship between observables. However, characterizing the manner in which information is distributed across a set of observables is computationally challenging and generally infeasible beyond a handful of measurements. Here, we propose a practical and general methodology that uses machine learning to decompose the information contained in a set of measurements by jointly optimizing a lossy compression of each measurement. Guided by the distributed information bottleneck as

SUBMITTER: Murphy KA 

PROVIDER: S-EPMC10990158 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature