Cited by 719Neural dynamics and brain functionVisual perception and processing mechanismsFunctional Brain Connectivity Studies

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

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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  • Decoding and other types of multivariate pattern analysis have helped reveal the content of regional representations, providing evidence that brain-computational models must incorporate.

    “Decoding and other types of multivariate pattern analysis have helped reveal the content of regional representations55,56,58,59, providing evidence that brain-computational models must incorporate.”

    From Cognitive computational neuroscience (2018) · cites this paper as [58]

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Research works printed but not linked: 2

  1. Connolly AC, Gobbini MI, Haxby JV. 2012a. Three virtues of similarity-based multi-voxel pattern analysis: an example from the human object vision pathway. In Understanding Visual Population Codes (UVPC): Toward A Common Multivariate Framework for Cell Recording and Functional Imaging, ed. N Kriegeskorte, G Kreiman, pp. 335-55. Cambridge, MA: MIT Press
  2. Constance Richter, Ian G. Woods, and Alexander F. Schier ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ 503 Indexes Cumulative Index of Contributing Authors, Volumes 28-37 ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ ♣ 533 Cumulative Index of Article Titles, Volumes 28-37 ♣ ♣ ♣ ♣ ♣ ♣ An corrections Review Neuroscience articles may be found at http://www.annualreviews.org/errata/neuro

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Authors
Haxby, J. V., Connolly, A. C., & Guntupalli, J. S.
Affiliations
Dartmouth College (US)
Venue
Annual Review of Neuroscience
Published
2014-07-08
Volume
37
Issue
1
Pages
435-456
ISSN
  • 0147-006X (ISSN-L)
  • 1545-4126
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
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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