Format

Send to

Choose Destination
See comment in PubMed Commons below
Annu Rev Neurosci. 2014;37:435-56. doi: 10.1146/annurev-neuro-062012-170325. Epub 2014 Jun 25.

Decoding neural representational spaces using multivariate pattern analysis.

Author information

1
Department of Psychological and Brain Sciences, Center for Cognitive Neuroscience, Dartmouth College, Hanover, New Hampshire 03755; email: james.v.haxby@dartmouth.edu , andrew.c.connolly@dartmouth.edu , swaroopgj@gmail.com.

Abstract

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.

KEYWORDS:

MVPA; RSA; fMRI; hyperalignment; neural decoding; population response

[Indexed for MEDLINE]
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Atypon
    Loading ...
    Support Center