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

Authors

1.2K

  1. NK
    Nikolaus Kriegeskorte

    MRC Cognition and Brain Sciences Unit, United Kingdom

    20 publications · 6.6K citations

    18

    h-index

  2. RM
    Radoslaw Martin Cichy

    Freie Universität Berlin, Germany

    12 publications · 704 citations

    11

    h-index

  3. JD
    Jörn Diedrichsen

    Western University, Canada

    9 publications · 742 citations

    9

    h-index

  4. CC
    Chuansheng Chen

    University of California, Irvine, United States

    8 publications · 299 citations

    8

    h-index

  5. DK
    Daniel Kaiser

    Freie Universität Berlin, Germany

    7 publications · 405 citations

    7

    h-index

1–5 of 1.2K

Venues

  1. Journal of Neuroscience

    59 publications · 4.6K citations

    38

    h-index

  2. NeuroImage

    44 publications · 2K citations

    26

    h-index

  3. Cerebral Cortex

    32 publications · 2K citations

    22

    h-index

  4. Scientific Reports

    17 publications · 1.1K citations

    15

    h-index

  5. Human Brain Mapping

    15 publications · 407 citations

    14

    h-index

1–5