Stramaglia Sebastiano, Scagliarini Tomas, Daniels Bryan C, Marinazzo Daniele
Dipartimento Interateneo di Fisica, Universitá degli Studi Aldo Moro, Bari and INFN, Bari, Italy.
Center of Innovative Technologies for Signal Detection and Processing (TIRES), Universitá degli Studi Aldo Moro, Bari, Italy.
Front Physiol. 2021 Jan 14;11:595736. doi: 10.3389/fphys.2020.595736. eCollection 2020.
We address the problem of efficiently and informatively quantifying how multiplets of variables carry information about the future of the dynamical system they belong to. In particular we want to identify groups of variables carrying redundant or synergistic information, and track how the size and the composition of these multiplets changes as the collective behavior of the system evolves. In order to afford a parsimonious expansion of shared information, and at the same time control for lagged interactions and common effect, we develop a dynamical, conditioned version of the O-information, a framework recently proposed to quantify high-order interdependencies via multivariate extension of the mutual information. The dynamic O-information, here introduced, allows to separate multiplets of variables which influence synergistically the future of the system from redundant multiplets. We apply this framework to a dataset of spiking neurons from a monkey performing a perceptual discrimination task. The method identifies synergistic multiplets that include neurons previously categorized as containing little relevant information individually.
我们解决了一个问题，即高效且信息丰富地量化变量多重态如何携带有关它们所属动态系统未来的信息。特别是，我们希望识别携带冗余或协同信息的变量组，并跟踪这些多重态的大小和组成如何随着系统的集体行为演变而变化。为了实现共享信息的简洁扩展，同时控制滞后相互作用和共同效应，我们开发了一种动态的、条件化的O信息版本，这是一个最近提出的通过互信息的多变量扩展来量化高阶相互依赖关系的框架。这里引入的动态O信息能够将协同影响系统未来的变量多重态与冗余多重态区分开来。我们将这个框架应用于一只执行感知辨别任务的猴子的尖峰神经元数据集。该方法识别出了协同多重态，其中包括以前被归类为单个包含很少相关信息的神经元。
