Probabilistic Population Coding

Probabilistic Population Coding is a neural coding framework that represents probability distributions over stimuli through the collective activity of neuronal populations, where the firing rates of neurons encode the parameters of a likelihood function in a manner that enables Bayesian inference via linear readout mechanisms.

31

Publications

4.8K

Citations

87

Authors

61

Institutions

Publications per year

2017–2026

8

Authors

87

  1. AP
    Alexandre Pouget

    University of Geneva, Switzerland

    7 publications · 3.3K citations

    7

    h-index

  2. WJ
    Wei Ji

    New York University, United States

    4 publications · 2.3K citations

    4

    h-index

  3. PE
    Peter E. Latham

    Oxford Centre for Computational Neuroscience, United Kingdom

    4 publications · 3K citations

    4

    h-index

  4. CM
    Cyriel M. A. Pennartz

    University of Amsterdam, Netherlands

    2 publications · 228 citations

    2

    h-index

  5. RK
    Roozbeh Kiani

    New York University, United States

    2 publications · 743 citations

    2

    h-index

1–5 of 87

Venues

  1. Nature Neuroscience

    5 publications · 2.8K citations

    5

    h-index

  2. Journal of Neuroscience

    3 publications · 315 citations

    3

    h-index

  3. Neuron

    3 publications · 790 citations

    3

    h-index

  4. Neural Computation

    2 publications · 73 citations

    2

    h-index

  5. Proceedings of the National Academy of Sciences

    PNAS · 2 publications · 220 citations

    2

    h-index

1–5