Probabilistic Population Codes

Probabilistic Population Codes is a computational neuroscience framework that represents probability distributions over stimuli through the collective activity of neural populations, where the firing rates of neurons encode posterior probabilities in a manner that supports Bayesian inference via linear decoding operations.

22

Publications

3.5K

Citations

62

Authors

41

Institutions

Publications per year

2017–2026

7

Authors

62

  1. AP
    Alexandre Pouget

    University of Geneva, Switzerland

    5 publications · 2.5K citations

    5

    h-index

  2. WJ
    Wei Ji

    New York University, United States

    4 publications · 2.3K citations

    4

    h-index

  3. RK
    Roozbeh Kiani

    New York University, United States

    2 publications · 743 citations

    2

    h-index

  4. PE
    Peter E. Latham

    Oxford Centre for Computational Neuroscience, United Kingdom

    2 publications · 2.2K citations

    2

    h-index

  5. JM
    Jeffrey M. Beck

    University of Rochester, United States

    2 publications · 2.2K citations

    2

    h-index

1–5 of 62

Venues

  1. Neuron

    3 publications · 790 citations

    3

    h-index

  2. Proceedings of the National Academy of Sciences

    PNAS · 2 publications · 220 citations

    2

    h-index

  3. Neural Computation

    2 publications · 184 citations

    2

    h-index

  4. Archive ouverte UNIGE (University of Geneva)

    Repository · 1 publications · 35 citations

    1

    h-index

  5. Multisensory Research

    1 publications · 40 citations

    1

    h-index

1–5