Neural Population Models

Neural Population Models is a mathematical framework that describes the collective dynamics of large groups of neurons by modeling their average activity and interactions, distinguishing it from single-neuron models through its focus on population-level statistical behavior and emergent network properties.

540

Publications

43K

Citations

1.3K

Authors

579

Institutions

Publications per year

2017–2026

162

Variants

Neural Population Modeling

Authors

1.3K

  1. KV
    Krishna V. Shenoy

    Stanford University, United States

    13 publications · 1.8K citations

    13

    h-index

  2. MT
    Matthew T. Kaufman

    Stanford University, United States

    8 publications · 1.8K citations

    8

    h-index

  3. JC
    Jonathan C. Kao

    Stanford University, United States

    8 publications · 1.2K citations

    8

    h-index

  4. GT
    Gašper Tkačik

    Institute of Science and Technology Austria, Austria

    8 publications · 566 citations

    8

    h-index

  5. MJ
    Michael J. Berry

    Princeton University, United States

    7 publications · 2.3K citations

    7

    h-index

1–5 of 1.3K

Venues

  1. PLoS Computational Biology

    29 publications · 2.1K citations

    22

    h-index

  2. Neural Computation

    29 publications · 1.6K citations

    20

    h-index

  3. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 20 publications · 554 citations

    15

    h-index

  4. Physical Review Letters

    PRL · 14 publications · 1.7K citations

    14

    h-index

  5. Network Computation in Neural Systems

    15 publications · 442 citations

    14

    h-index

1–5