TY - JOUR T1 - A Tutorial for Information Theory in Neuroscience JF - eneuro JO - eNeuro DO - 10.1523/ENEURO.0052-18.2018 SP - ENEURO.0052-18.2018 AU - Nicholas M. Timme AU - Christopher Lapish Y1 - 2018/06/29 UR - http://www.eneuro.org/content/early/2018/06/29/ENEURO.0052-18.2018.abstract N2 - 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 non-linear, 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 non-linear 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.Significance Statement A primary function of the brain is to process and store information. Therefore, it makes sense to analyze the behavior of the brain using information theory, a statistical tool especially designed to quantify information. Furthermore, given improvements in data gathering techniques, the power of information theory to analyze large, complex data sets is particularly relevant. In this tutorial, we provide a thorough introduction to information theory and how it can be applied to data gathered from the brain. Our primary audience for this tutorial is researchers new to information theory. We provide numerous intuitive examples including small abstract systems, small and large brain circuits, systems from famous neuroscience experiments, and free software to implement all calculations and models presented herein. ER -