Representational Geometry

Representational Geometry is a theoretical framework in computational neuroscience that characterizes the geometric structure of activity patterns in neural populations, distinguishing itself by formalizing how stimulus representations are organized in high-dimensional neural state spaces according to their informational and functional relationships.

89

Publications

8.3K

Citations

276

Authors

144

Institutions

Publications per year

2017–2026

54

Authors

276

  1. NK
    Nikolaus Kriegeskorte

    MRC Cognition and Brain Sciences Unit, United Kingdom

    8 publications · 2K citations

    8

    h-index

  2. SF
    Stefano Fusi

    Columbia University, United States

    8 publications · 425 citations

    8

    h-index

  3. JD
    Jörn Diedrichsen

    Western University, Canada

    5 publications · 462 citations

    5

    h-index

  4. UR
    Ueli Rutishauser

    Cedars-Sinai Medical Center, United States

    5 publications · 280 citations

    5

    h-index

  5. AC
    Andrew C. Connolly

    Dartmouth College, United States

    4 publications · 1.1K citations

    4

    h-index

1–5 of 276

Venues

  1. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 14 publications · 304 citations

    12

    h-index

  2. NeuroImage

    4 publications · 180 citations

    4

    h-index

  3. Nature Communications

    4 publications · 318 citations

    4

    h-index

  4. Cerebral Cortex

    3 publications · 408 citations

    3

    h-index

  5. Science

    3 publications · 1.6K citations

    3

    h-index

1–5