Representational Similarity Analysis
Representational Similarity Analysis is a computational method in cognitive neuroscience that compares the spatial patterns of neural responses across different stimuli by quantifying the similarity structure of their distributed activity representations, typically using pairwise correlation or distance metrics to relate neural data to models or behavioral measures.
376
Publications
27.3K
Citations
1.2K
Authors
472
Institutions
Publications per year
2017–2026
279
Parents
376
A Toolbox for Representational Similarity Analysis
Hamed Nili, Cai Wingfield, Alexander Walther et al. · PLoS Computational Biology · 2014 · 1.1K citations · Full text
Similarity of Neural Network Representations Revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee et al. · arXiv (Cornell University) · 2019 · 432 citations · Full text
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MRC Cognition and Brain Sciences Unit, United Kingdom
20 publications · 6.6K citations
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University of Cambridge
Cambridge, United Kingdom
21 h-index
57 publications
6.5K citations

University of Trento
Trento, Italy
19 h-index
44 publications
5.4K citations

MRC Cognition and Brain Sciences Unit
Cambridge, United Kingdom
19 h-index
47 publications
9.2K citations
Harvard University
Cambridge, United States
18 h-index
33 publications
2.3K citations
Dartmouth College
Hanover, United States
17 h-index
44 publications
5.4K citations

Beijing Normal University
Beijing, China
17 h-index
70 publications
3.6K citations
University College London
London, United Kingdom
16 h-index
33 publications
2.2K citations

Western University
London, Canada
14 h-index
30 publications
1.4K citations
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59 publications · 4.6K citations
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44 publications · 2K citations
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32 publications · 2K citations
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17 publications · 1.1K citations
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15 publications · 407 citations
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