Neural Representation Learning

Neural Representation Learning is a computational framework within neural encoding that identifies and constructs invariant and meaningful patterns of neural activity to represent sensory, cognitive, or behavioral variables through learned transformations of neural population data.

1.2K

Publications

155.8K

Citations

3.6K

Authors

1K

Institutions

Publications per year

2017–2026

717

Authors

3.6K

  1. JJ
    James J. DiCarlo

    McGovern Institute for Brain Research, United States

    13 publications · 2.9K citations

    12

    h-index

  2. TP
    Tomaso Poggio

    Massachusetts Institute of Technology, United States

    11 publications · 237 citations

    10

    h-index

  3. TL
    Tianming Liu

    University of Georgia, United States

    10 publications · 460 citations

    10

    h-index

  4. DY
    Daniel Yamins

    Stanford University, United States

    9 publications · 2.3K citations

    9

    h-index

  5. YK
    Yukiyasu Kamitani

    Kyoto University, Japan

    9 publications · 717 citations

    9

    h-index

1–5 of 3.6K

Venues

  1. arXiv (Cornell University)

    Repository · 131 publications · 6K citations

    37

    h-index

  2. NeuroImage

    35 publications · 2.4K citations

    28

    h-index

  3. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 63 publications · 2.3K citations

    25

    h-index

  4. Neural Computation

    32 publications · 3K citations

    24

    h-index

  5. Neural Networks

    34 publications · 2.7K citations

    23

    h-index

1–5