论文标题
量化O信息的量化动力学高阶相互依赖性：神经尖峰动力学的应用
Quantifying dynamical high-order interdependencies from the O-information: an application to neural spiking dynamics
论文作者
论文摘要
我们解决了有效且信息性地量化变量的多重内容的问题，以携带有关其属于动力学系统的未来的信息。特别是我们要确定带有冗余或协同信息的变量组，并跟踪随着系统的集体行为的发展，这些多重组的大小和组成如何变化。为了提供共享信息的简约扩展，并同时控制滞后相互作用和共同效果，我们开发了O-Information的动力学条件版本，该框架最近提出了一个框架，该框架通过共同信息的多元扩展来量化高阶相互依赖性。因此，我们获得了转移熵的扩展，其中协同和冗余效应分离出来。我们将此框架应用于执行感知歧视任务的猴子的尖峰神经元数据集。该方法确定了包括以前分别包含几乎没有相关信息的神经元的神经元的协同多重组。
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. We thus obtain an expansion of the transfer entropy in which synergistic and redundant effects are separated. 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.
