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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Support-vector networks
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Support-Vector Networks
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565
Self-Organizing Maps
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-citation
Representational 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-citation
Information-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

Show all 80 references

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

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Meshulam, M., Hasenfratz, L., Hillman, H., Liu, Y.-F., Nguyen, M., Norman, K. A., & Hasson, U. (2020) · doi:10.1101/2020.05.05.079384 · Cited by 85
“These methods, together with representational similarity analysis (RSA), have made it possible to delineate the fine-grained structure of neural representations of learned knowledge, and to link neural patterns to specific knowledge across multiple domains (Bauer and Just, 2019; Haxby et al., 2014; Hsu et al., 2014; M…”
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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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Decoding Neural Representational Spaces Using Multivariate Pattern Analysis

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