Stochastic Neuron Models

Stochastic Neuron Models is a class of computational neuroscience models that incorporate probabilistic mechanisms to describe the inherent variability and randomness in neuronal dynamics, distinguishing them from deterministic models by explicitly accounting for noise in membrane potential, synaptic transmission, or spiking behavior.

890

Publications

59.4K

Citations

1.8K

Authors

763

Institutions

Publications per year

2017–2026

186

Authors

1.8K

  1. PL
    Petr Lánský

    Czech Academy of Sciences, Institute of Physiology, Czechia

    18 publications · 660 citations

    15

    h-index

  2. WG
    Wulfram Gerstner

    École Polytechnique Fédérale de Lausanne, Switzerland

    12 publications · 1.4K citations

    12

    h-index

  3. BL
    Benjamin Lindner

    Bernstein Center for Computational Neuroscience Berlin, Germany

    15 publications · 758 citations

    12

    h-index

  4. KP
    Khashayar Pakdaman

    Inserm, France

    14 publications · 614 citations

    12

    h-index

  5. HC
    Henry C. Tuckwell

    University of British Columbia, Canada

    11 publications · 464 citations

    11

    h-index

1–5 of 1.8K

Venues

  1. 30

    h-index

  2. Physical Review E

    PRE · 48 publications · 1.9K citations

    28

    h-index

  3. Neural Computation

    56 publications · 3.7K citations

    26

    h-index

  4. Physical Review Letters

    PRL · 29 publications · 3.5K citations

    25

    h-index

  5. PLoS Computational Biology

    25 publications · 1.8K citations

    20

    h-index

1–5