Bayesian Neural Decoding

Bayesian Neural Decoding is a neural decoding framework that uses Bayesian inference to estimate the most probable stimulus or cognitive state given observed neural activity, by combining a prior distribution over possible states with a likelihood model of the neural responses.

46

Publications

2.8K

Citations

160

Authors

84

Institutions

Publications per year

2017–2026

22

Authors

160

  1. LP
    Liam Paninski

    Columbia University, United States

    5 publications · 242 citations

    5

    h-index

  2. EN
    Emery N. Brown

    Massachusetts General Hospital, United States

    4 publications · 139 citations

    4

    h-index

  3. EB
    Eleanor Batty

    Stanford University, United States

    3 publications · 145 citations

    3

    h-index

  4. MA
    Matthew A. Wilson

    Massachusetts Institute of Technology, United States

    3 publications · 192 citations

    3

    h-index

  5. TN
    Timothy N. Rubin

    Indiana University Bloomington, United States

    2 publications · 169 citations

    2

    h-index

1–5 of 160

Venues

  1. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 5 publications · 128 citations

    5

    h-index

  2. PLoS Computational Biology

    4 publications · 266 citations

    4

    h-index

  3. Neural Computation

    4 publications · 570 citations

    4

    h-index

  4. Hippocampus

    3 publications · 186 citations

    3

    h-index

  5. Journal of Neurophysiology

    JN · 3 publications · 425 citations

    3

    h-index

1–5