Nonlinear Decoders

Nonlinear Decoders is a neural decoding method that employs nonlinear transformations to map neural activity patterns into behavioral or cognitive variables, distinguishing it from linear decoders by its capacity to model complex, non-additive relationships in neural data.

26

Publications

1.1K

Citations

87

Authors

51

Institutions

Publications per year

2017–2026

6

Authors

87

  1. AZ
    Avideh Zakhor

    University of California, Berkeley, United States

    2 publications · 67 citations

    2

    h-index

  2. SH
    S. Hein

    University of California, Berkeley, United States

    2 publications · 67 citations

    2

    h-index

  3. GW
    Gregory W. Schwartz

    Northwestern University, United States

    1 publications · 26 citations

    1

    h-index

  4. FM
    Farhad Mehdizadeh

    Islamic Azad University of Tabriz, Iran

    1 publications · 70 citations

    1

    h-index

  5. AG
    Ankit Gupta

    University of Illinois Chicago, United States

    1 publications · 45 citations

    1

    h-index

1–5 of 87

Venues

  1. Journal of Neurophysiology

    JN · 3 publications · 164 citations

    3

    h-index

  2. IEEE Transactions on Information Theory

    2 publications · 68 citations

    2

    h-index

  3. IEEE Transactions on Communications

    2 publications · 63 citations

    2

    h-index

  4. arXiv (Cornell University)

    Repository · 2 publications · 83 citations

    2

    h-index

  5. Electronics Letters

    1 publications · 21 citations

    1

    h-index

1–5