The DOI matches its Crossref record; 82 of the 82 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565
Kohonen, T. (1995) · Springer series in information sciences · doi:10.1007/978-3-642-56927-2 · Cited by 16,032
Self-organized formation of topologically correct feature maps
Kohonen, T. (1982) · Biological Cybernetics · doi:10.1007/bf00337288 · Cited by 9,529
The fusiform face area: a module in human extrastriate cortex specialized for face perception
Kanwisher, N., McDermott, J., & Chun, M. M. (1997) · The Journal of Neuroscience · doi:10.1523/jneurosci.17-11-04302.1997 · Cited by 7,738
Distributed and overlapping representations of faces and objects in ventral temporal cortex
Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., & Pietrini, P. (2001) · Science · doi:10.1126/science.1063736 · Cited by 3,940
Self-citationRepresentational similarity analysis-connecting the branches of systems neuroscience
Kriegeskorte, N. (2008) · Frontiers in Systems Neuroscience · doi:10.3389/neuro.06.004.2008 · Cited by 3,355
Circular analysis in systems neuroscience: the dangers of double dipping
Kriegeskorte, N., Simmons, W. K., Bellgowan, P. S. F., & Baker, C. I. (2009) · Nature Neuroscience · doi:10.1038/nn.2303 · Cited by 2,696
Beyond mind-reading: multi-voxel pattern analysis of fMRI data
Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.07.005 · Cited by 2,271
Self-citationInformation-based functional brain mapping
Kriegeskorte, N., Goebel, R., & Bandettini, P. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0600244103 · Cited by 2,113
Research works printed but not linked: 3
- Abdi H, 2012, Comp. Math. Methods Med., 2012, 634165
- Connolly AC, 2012, Understanding Visual Population Codes (UVPC): Toward A Common Multivariate Framework for Cell Recording and Functional Imaging, 335
- O'Toole AJ, 2007, J. Cogn. Neurosci., 191, 735
Cited by
Sorted by citations
Pisner, D., & Schnyer, D. M. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-815739-8.00006-7 · Cited by 1,239
Lindquist, K. A., Satpute, A. B., Wager, T. D., Weber, J., & Barrett, L. F. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhv001 · Cited by 713
Reliability of dissimilarity measures for multi-voxel pattern analysis
Walther, A., Nili, H., Ejaz, N., Alink, A., Kriegeskorte, N., & Diedrichsen, J. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.12.012 · Cited by 567
Cognitive computational neuroscience
Kriegeskorte, N., & Douglas, P. K. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0210-5 · Cited by 535
On the stability of BOLD fMRI correlations
Laumann, T. O., Snyder, A. Z., Mitra, A., Gordon, E. M., Gratton, C., Adeyemo, B., Gilmore, A. W., Nelson, S. M., Berg, J. J., Greene, D. J., et al. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw265 · Cited by 516
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 ›
Are you an author, or do you see a problem with this page? Request a review
