Two-layer Neural Computation

Two-Layer Neural Computation is a computational framework in neuroscience and artificial intelligence that models the integration of synaptic inputs through two hierarchically organized layers of nonlinear processing units, typically representing the interaction between dendritic compartments and somatic output in neurons, thereby enabling enhanced representational capacity beyond single-layer integration.

285

Publications

23.5K

Citations

703

Authors

316

Institutions

Publications per year

2017–2026

144

Authors

703

  1. YL
    Yuanzhi Li

    Princeton University, United States

    7 publications · 668 citations

    7

    h-index

  2. AM
    Andrea Montanari

    Stanford University, United States

    6 publications · 249 citations

    6

    h-index

  3. FK
    Florent Krząkała

    Centre National de la Recherche Scientifique, France

    6 publications · 178 citations

    6

    h-index

  4. LZ
    Lenka Zdeborová

    Centre National de la Recherche Scientifique, France

    6 publications · 178 citations

    6

    h-index

  5. DS
    David Saad

    Aston University, United Kingdom

    5 publications · 535 citations

    5

    h-index

1–5 of 703

Venues

  1. arXiv (Cornell University)

    Repository · 43 publications · 2.2K citations

    23

    h-index

  2. IEEE Transactions on Neural Networks

    17 publications · 5.2K citations

    15

    h-index

  3. 5

    h-index

  4. Neural Computation

    5 publications · 101 citations

    5

    h-index

  5. Computers & Mathematics with Applications

    4 publications · 207 citations

    4

    h-index

1–5