Encoding and Decoding Models in Cognitive Electrophysiology.
Encoding and Decoding Models in Cognitive Electrophysiology.
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DOI：
10.3389/fnsys.2017.00061
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发表时间：
2017
影响因子：
3
通讯作者：
Theunissen FE
中科院分区：
文献类型：
--
作者：
Holdgraf CR;Rieger JW;Micheli C;Martin S;Knight RT;Theunissen FE
认知神经科学见证了人脑记录数据的大小和复杂性以及可用于分析这些数据的计算工具的快速增长。这种数据爆炸导致越来越多地使用基于模型的多变量方法来提出神经科学问题，使科学家能够利用单个数据集研究多种假设，使用复杂的、随时间变化的刺激，并在更自然的条件下研究人脑。这些工具以“编码”模型和“解码”模型的形式出现，其中刺激特征用于模拟大脑活动，而“解码”模型则使用神经特征来生成刺激输出。在这里，我们回顾了认知电生理学中编码和解码模型的现状，并为在这个新兴领域进行实验和分析提供了实用指南。我们的示例侧重于在人类语言和听力研究中使用线性模型。我们展示了如何从自然声音中计算听觉感受野，以及如何解码神经记录以预测语音。本文旨在成为这些方法的有用教程，并实用介绍如何使用机器学习和应用统计学来构建神经活动模型。我们讨论的数据分析方法也可以应用于其他感觉方式、运动系统和认知系统，我们将介绍这些领域的一些例子。此外，还公开了一系列 Jupyter 笔记本，作为本文所涵盖材料的补充，提供了 Python 预测建模的代码示例和教程。目的是提供对人脑数据预测模型的实际理解，并提出进行这些分析的最佳实践。
Cognitive neuroscience has seen rapid growth in the size and complexity of data recorded from the human brain as well as in the computational tools available to analyze this data. This data explosion has resulted in an increased use of multivariate, model-based methods for asking neuroscience questions, allowing scientists to investigate multiple hypotheses with a single dataset, to use complex, time-varying stimuli, and to study the human brain under more naturalistic conditions. These tools come in the form of “Encoding” models, in which stimulus features are used to model brain activity, and “Decoding” models, in which neural features are used to generated a stimulus output. Here we review the current state of encoding and decoding models in cognitive electrophysiology and provide a practical guide toward conducting experiments and analyses in this emerging field. Our examples focus on using linear models in the study of human language and audition. We show how to calculate auditory receptive fields from natural sounds as well as how to decode neural recordings to predict speech. The paper aims to be a useful tutorial to these approaches, and a practical introduction to using machine learning and applied statistics to build models of neural activity. The data analytic approaches we discuss may also be applied to other sensory modalities, motor systems, and cognitive systems, and we cover some examples in these areas. In addition, a collection of Jupyter notebooks is publicly available as a complement to the material covered in this paper, providing code examples and tutorials for predictive modeling in python. The aim is to provide a practical understanding of predictive modeling of human brain data and to propose best-practices in conducting these analyses.
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