Population Neural Models

Population Neural Models is a computational framework in neural system modeling that characterizes the collective dynamics of large groups of neurons through aggregated variables, distinguishing itself from single-neuron or circuit-level models by focusing on statistical population behaviors and emergent network properties.

248

Publications

24K

Citations

636

Authors

334

Institutions

Publications per year

2017–2026

80

Authors

636

  1. KV
    Krishna V. Shenoy

    Stanford University, United States

    7 publications · 1K citations

    7

    h-index

  2. JC
    Jonathan C. Kao

    Stanford University, United States

    6 publications · 995 citations

    6

    h-index

  3. DT
    Daniel Tranchina

    Courant Institute of Mathematical Sciences, United States

    6 publications · 491 citations

    6

    h-index

  4. MJ
    Michael J. Berry

    Princeton University, United States

    5 publications · 2K citations

    5

    h-index

  5. HS
    Haim Sompolinsky

    Hebrew University of Jerusalem, Israel

    5 publications · 908 citations

    5

    h-index

1–5 of 636

Venues

  1. PLoS Computational Biology

    16 publications · 1K citations

    14

    h-index

  2. Neural Computation

    12 publications · 489 citations

    12

    h-index

  3. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 9 publications · 260 citations

    9

    h-index

  4. Biological Cybernetics

    8 publications · 577 citations

    8

    h-index

  5. Nature Neuroscience

    8 publications · 2.5K citations

    8

    h-index

1–5