Neural Encoding Models

Neural Encoding Models is a computational framework that specifies how neural responses are predicted from external stimuli by modeling the transformation of sensory inputs into patterns of neuronal activity.

490

Publications

38.6K

Citations

1.4K

Authors

495

Institutions

Publications per year

2017–2026

221

Authors

1.4K

  1. JW
    Jonathan W. Pillow

    Princeton University, United States

    9 publications · 345 citations

    9

    h-index

  2. AG
    Alexander G. Huth

    The University of Texas at Austin, United States

    8 publications · 389 citations

    8

    h-index

  3. LP
    Liam Paninski

    Columbia University, United States

    5 publications · 994 citations

    5

    h-index

  4. SJ
    Shailee Jain

    The University of Texas at Austin, United States

    5 publications · 298 citations

    5

    h-index

  5. ND
    Nai Ding

    Zhejiang University, China

    5 publications · 1.2K citations

    5

    h-index

1–5 of 1.4K

Venues

  1. PLoS Computational Biology

    24 publications · 1.9K citations

    21

    h-index

  2. Journal of Neuroscience

    23 publications · 1.9K citations

    20

    h-index

  3. Neural Computation

    22 publications · 1.8K citations

    19

    h-index

  4. eLife

    17 publications · 1.5K citations

    17

    h-index

  5. Journal of Neurophysiology

    JN · 18 publications · 800 citations

    16

    h-index

1–5