The brain can be regarded as an information processing system in which neurons store and propagate information about external stimuli and internal processes. Therefore, estimating interactions between neural activity at the cellular scale has significant implications in understanding how neuronal circuits encode and communicate information across brain areas to generate behavior. While the number of simultaneously recorded neurons is growing exponentially, current methods relying only on pairwise statistical dependencies still suffer from a number of conceptual and technical challenges that preclude experimental breakthroughs describing neural information flows. In this review, we examine the evolution of the field over the years, starting from descriptive statistics to model-based and model-free approaches. Then, we discuss in detail the Granger Causality framework, which includes many popular state-of-the-art methods and we highlight some of its limitations from a conceptual and practical estimation perspective. Finally, we discuss directions for future research, including the development of theoretical information flow models and the use dimensionality reduction techniques to extract relevant interactions from large-scale recording datasets.

大脑可以看作是一个信息处理系统，其中神经元存储和传播有关外部刺激和内部过程的信息。因此，在细胞规模上估计神经活动之间的相互作用对理解神经元回路如何编码和交流大脑区域信息以产生行为具有重要意义。虽然同时记录的神经元的数量呈指数增长，但是仅依赖于成对统计依赖性的当前方法仍然遭受许多概念和技术挑战，无法描述神经信息流的实验突破。在这篇综述中，我们研究了该领域多年来的发展，从描述性统计数据到基于模型和无模型的方法。然后，我们将详细讨论Granger因果关系框架，其中包括许多流行的最新方法，并且从概念和实际评估的角度强调了它的一些局限性。最后，我们讨论了未来研究的方向，包括理论信息流模型的开发以及使用降维技术从大规模记录数据集中提取相关的相互作用。