Cited by 718Neural dynamics and brain functionVisual perception and processing mechanismsFunctional Brain Connectivity Studies

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Decoding Neural Representational Spaces Using Multivariate Pattern Analysis

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Haxby, James V., Connolly, Andrew C., Guntupalli, J. Swaroop·2014·Annual Review of Neuroscience

DOI 10.1146/annurev-neuro-062012-170325

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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.

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Authors: Haxby, J. V., Connolly, A. C., & Guntupalli, J. S.
Affiliations: Dartmouth College (US)
ORCID: Haxby, James V.
Connolly, Andrew C.
Guntupalli, J. Swaroop
Venue: Annual Review of Neuroscience
Publisher: Annual Reviews
Published: 2014-07-08
Volume: 37
Issue: 1
Pages: 435-456
ISSN: 0147-006X (ISSN-L)
1545-4126
DOI: 10.1146/annurev-neuro-062012-170325
OpenAlex: W2146246269
Cited by: 717
References: 80
Citations by year: 2026: 34 · 2025: 64 · 2024: 47 · 2023: 61 · 2022: 67 · 2021: 69 · 2020: 87 · 2019: 78 · 2018: 73 · 2017: 57 · 2016: 41 · 2015: 35 · 2014: 4
FWCI: 4.41
Type: journal article
Language: en
Open access: bronze
doi.org
Locations: Annual Review of Neuroscience (publishedVersion)
PubMed (publishedVersion)
Institutional Research Information System (Università degli Studi di Trento) (submittedVersion)
INFM-OAR (INFN Catania) (submittedVersion, other-oa)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords: neural decoding, multivariate pattern analysis, representational similarity analysis, hyperalignment, neural coding, representational space, neural code
MeSH terms: Animals, Brain Mapping, Humans, Image Processing, Computer-Assisted, Models, Neurological, Neurons

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

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Decoding Neural Representational Spaces Using Multivariate Pattern Analysis

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