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Decoding Neural Representational Spaces Using Multivariate Pattern Analysis
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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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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
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Goldstein, A., Grinstein-Dabush, A., Aubrey, B., Schain, M., Nastase, S. A., Zada, Z., Ham, E., Hong, Z., Feder, A., Gazula, H., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.01.482586 · Cited by 6
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Tang, M. F., Smout, C., Arabzadeh, E., & Mattingley, J. B. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/213710 · Cited by 6
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Newsome, P., Vaccaro, A. G., Cárdenas, S. I., Valen, N. A., Waizman, Y. H., Aviv, E. C., León, G. A., Kaplan, J., & Saxbe, D. (2025) · Human Brain Mapping · doi:10.1002/hbm.70324 · Cited by 3
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Fuchigami, T., Shikauchi, Y., Nakae, K., Shikauchi, M., Ogawa, T., & Ishii, S. (2018) · Scientific Reports · doi:10.1038/s41598-018-30676-3 · Cited by 4
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Wang, Y., Sheng, Z., Zheng, J., Chen, H., & Chen, H. (2015) · IEEE Transactions on Autonomous Mental Development · doi:10.1109/tamd.2015.2427341 · Cited by 3
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Yang, L., Zhen, H., Li, L., Li, Y., Zhang, H., Xie, X., & Zhang, R. (2023) · Fundamental Research · doi:10.1016/j.fmre.2023.08.010 · Cited by 3
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Yoshimoto, T., Tokunaga, K., & Chikazoe, J. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120911 · Cited by 3
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Sevcencu, C., & Struijk, J. J. (2018) · Bioelectronics in Medicine · doi:10.2217/bem-2017-0014 · Cited by 3
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Fırat, O., Aksan, E., Öztekin, I., & Vural, F. T. Y. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.7522 · Cited by 5
Zhou, F., Li, J., Zhao, W., Xu, L., Zheng, X., Fu, M., Yao, S., Kendrick, K. M., Wager, T. D., & Becker, B. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.24.963595 · Cited by 3
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Jabakhanji, R., Vigotsky, A. D., Bielefeld, J., Huang, L. Q., Baliki, M. N., Iannetti, G. D., & Apkarian, A. V. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.18.423495 · Cited by 3
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Taschereau‐Dumouchel, V., Liu, K., & Lau, H. C. (2018) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/c7jfd · Cited by 4
Wu, L., Zhi, H., Liu, J., & Yu, H. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107335 · Cited by 2
Bai, D., Zhang, T., Zhang, Q., Zhang, J., Jin, Z., & Li, L. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.132094 · Cited by 2
Gohel, B., Lee, P., & Jeong, Y. (2016) · Brain Research · doi:10.1016/j.brainres.2016.04.068 · Cited by 2
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Apperly, I. A. (2021) · Springer eBooks · doi:10.1007/978-3-030-51890-5_18 · Cited by 2
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Rhythmic sampling and competition of target and distractor in a motion detection task
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Boosting hyperalignment performance with age-specific templates
Zhang, Y., Gobbini, M. I., Haxby, J. V., & Feilong, M. (2026) · eLife · doi:10.7554/elife.110566.3 · Cited by 2
Self-citationShun, L., & Liu, X. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121140 · Cited by 2
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Yang, A., Jinhua, T., Wang, W., Liu, J., Zhou, K., & Zhou, K. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121296 · Cited by 2
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
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