Neural Sampling

Neural Sampling is a computational framework within population coding that models perceptual inference and decision-making by representing probability distributions over stimuli through the stochastic activity patterns of neural populations, where each neuron's spiking activity corresponds to a sample from the underlying posterior distribution.

64

Publications

3.4K

Citations

187

Authors

78

Institutions

Publications per year

2017–2026

27

Authors

187

  1. EN
    Emre Neftci

    University of California, Irvine, United States

    4 publications · 315 citations

    4

    h-index

  2. TT
    Tianqi Tang

    Harbin Institute of Technology, China

    3 publications · 108 citations

    3

    h-index

  3. HY
    Huazhong Yang

    Tsinghua University, China

    3 publications · 108 citations

    3

    h-index

  4. SD
    Suman Datta

    Pennsylvania State University, United States

    3 publications · 113 citations

    3

    h-index

  5. LX
    Lixue Xia

    Tsinghua University, China

    3 publications · 108 citations

    3

    h-index

1–5 of 187

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 68 citations

    4

    h-index

  2. Journal of Neuroscience Methods

    3 publications · 65 citations

    3

    h-index

  3. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 3 publications · 33 citations

    3

    h-index

  4. Proceedings of the National Academy of Sciences

    PNAS · 3 publications · 289 citations

    3

    h-index

  5. Neuron

    3 publications · 644 citations

    3

    h-index

1–5