Neural Decoding Models

Neural Decoding Models is a computational framework that, by inferring intended outputs or cognitive states from neural activity patterns, distinguishes itself within neural system modeling by translating biological neural representations into interpretable signals or behavioral predictions.

225

Publications

14.8K

Citations

883

Authors

391

Institutions

Publications per year

2017–2026

134

Authors

883

  1. SN
    Shinji Nishimoto

    The University of Osaka, Japan

    5 publications · 343 citations

    5

    h-index

  2. EF
    Edward F. Chang

    University of California, San Francisco, United States

    5 publications · 1.3K citations

    5

    h-index

  3. KV
    Krishna V. Shenoy

    Stanford University, United States

    4 publications · 611 citations

    4

    h-index

  4. JL
    Jiyi Li

    University of Electronic Science and Technology of China, China

    3 publications · 57 citations

    3

    h-index

  5. MS
    Maneesh Sahani

    University College London, United Kingdom

    3 publications · 303 citations

    3

    h-index

1–5 of 883

Venues

  1. NeuroImage

    20 publications · 1.2K citations

    16

    h-index

  2. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 11 publications · 246 citations

    10

    h-index

  3. Journal of Neural Engineering

    9 publications · 326 citations

    9

    h-index

  4. Nature Communications

    10 publications · 732 citations

    9

    h-index

  5. Scientific Reports

    8 publications · 292 citations

    8

    h-index

1–5