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Decoding Neural Representational Spaces Using Multivariate Pattern Analysis
Open accessAbstract
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 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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Metadata
- Authors
- Haxby, J. V., Connolly, A. C., & Guntupalli, J. S.
- Affiliations
- Dartmouth College (US)
- 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
- 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
About these checks
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.
What counts as a claim ›How we measure confidence ›How we check this ›
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