Department of Psychology, Indiana University - Purdue University Indianapolis, 402 N. Blackford St, Indianapolis, IN 46202.
eNeuro. 2018 Sep 11;5(3). doi: 10.1523/ENEURO.0052-18.2018. eCollection 2018 May-Jun.
Understanding how neural systems integrate, encode, and compute information is central to understanding brain function. Frequently, data from neuroscience experiments are multivariate, the interactions between the variables are nonlinear, and the landscape of hypothesized or possible interactions between variables is extremely broad. Information theory is well suited to address these types of data, as it possesses multivariate analysis tools, it can be applied to many different types of data, it can capture nonlinear interactions, and it does not require assumptions about the structure of the underlying data (i.e., it is model independent). In this article, we walk through the mathematics of information theory along with common logistical problems associated with data type, data binning, data quantity requirements, bias, and significance testing. Next, we analyze models inspired by canonical neuroscience experiments to improve understanding and demonstrate the strengths of information theory analyses. To facilitate the use of information theory analyses, and an understanding of how these analyses are implemented, we also provide a free MATLAB software package that can be applied to a wide range of data from neuroscience experiments, as well as from other fields of study.
理解神经系统如何整合、编码和计算信息是理解大脑功能的核心。通常，神经科学实验的数据是多元的，变量之间的相互作用是非线性的，并且变量之间假设或可能的相互作用的范围非常广泛。信息论非常适合处理这些类型的数据，因为它具有多元分析工具，可以应用于许多不同类型的数据，可以捕捉非线性相互作用，并且不需要对基础数据的结构做出假设（即，它是独立于模型的）。在本文中，我们沿着信息论的数学原理，以及与数据类型、数据分箱、数据数量要求、偏差和显著性检验相关的常见逻辑问题进行探讨。接下来，我们分析受经典神经科学实验启发的模型，以提高对信息论分析的理解并展示其优势。为了便于使用信息论分析，并了解这些分析是如何实现的，我们还提供了一个免费的 MATLAB 软件包，可应用于来自神经科学实验以及其他研究领域的广泛数据。
