Random Network Models

Random Network Models is a class of computational neural network models in which synaptic connections between neurons are established probabilistically according to specified statistical rules, distinguishing it from structured or biologically constrained network architectures by its emphasis on randomness and emergent dynamical properties.

682

Publications

50.1K

Citations

1.5K

Authors

729

Institutions

Publications per year

2017–2026

122

Authors

1.5K

  1. EG
    Erol Gelenbe

    Imperial College London, United Kingdom

    16 publications · 1.8K citations

    12

    h-index

  2. SK
    Stuart Kauffman

    Institute for Systems Biology, United States

    9 publications · 1.6K citations

    9

    h-index

  3. MG
    Mark G. Sceats

    The University of Sydney, Australia

    8 publications · 554 citations

    8

    h-index

  4. CG
    Carlos Gershenson

    Universidad Nacional Autónoma de México, Mexico

    6 publications · 334 citations

    6

    h-index

  5. BL
    Bartolo Luque

    Universidad Politécnica de Madrid, Spain

    6 publications · 186 citations

    6

    h-index

1–5 of 1.5K

Venues

  1. Physical Review Letters

    PRL · 28 publications · 4.6K citations

    24

    h-index

  2. Physical Review E

    PRE · 35 publications · 1.7K citations

    22

    h-index

  3. Physical review. B, Condensed matter

    PRB · 25 publications · 1.8K citations

    18

    h-index

  4. The Journal of Chemical Physics

    15 publications · 884 citations

    15

    h-index

  5. Journal of Non-Crystalline Solids

    16 publications · 2.2K citations

    15

    h-index

1–5