Department of Psychological and Brain Sciences, Center for Cognitive Neuroscience, Dartmouth College, Hanover, New Hampshire 03755; email:
Annu Rev Neurosci. 2014;37:435-56. doi: 10.1146/annurev-neuro-062012-170325. Epub 2014 Jun 25.
A major challenge for systems neuroscience is to break the neural code. Computational algorithms for encoding information into neural activity and extracting information from measured activity afford understanding of how percepts, memories, thought, and knowledge are represented in patterns of brain activity. The past decade and a half has seen significant advances in the development of methods for decoding human neural activity, such as multivariate pattern classification, representational similarity analysis, hyperalignment, and stimulus-model-based encoding and decoding. This article reviews these advances and integrates neural decoding methods into a common framework organized around the concept of high-dimensional representational spaces.
系统神经科学面临的一个主要挑战是破解神经密码。将信息编码为神经活动并从测量的活动中提取信息的计算算法使我们能够理解感知、记忆、思维和知识是如何以大脑活动模式来表示的。在过去的十五年中，用于解码人类神经活动的方法取得了重大进展，例如多元模式分类、表示相似性分析、超对准和基于刺激模型的编码和解码。本文回顾了这些进展，并将神经解码方法整合到一个以高维表示空间概念为组织的通用框架中。
