Event-driven Computation

Event-Driven Computation is a computational paradigm in spiking neural networks wherein processing occurs asynchronously and selectively in response to discrete synaptic events or spikes, differing from clock-driven approaches by eliminating fixed-timestep updates and enabling computations only when input or internal state changes trigger an event.

153

Publications

11.5K

Citations

571

Authors

170

Institutions

Publications per year

2017–2026

98

Authors

571

  1. SF
    Steve Furber

    University of Manchester, United Kingdom

    9 publications · 1.7K citations

    9

    h-index

  2. FG
    Francesco Galluppi

    University of Manchester, United Kingdom

    8 publications · 1.5K citations

    8

    h-index

  3. SL
    Shih‐Chii Liu

    ETH Zurich, Switzerland

    7 publications · 1.8K citations

    7

    h-index

  4. PL
    Peng Li

    Texas A&M University, United States

    6 publications · 249 citations

    6

    h-index

  5. HT
    Huajin Tang

    Sichuan University, China

    6 publications · 410 citations

    6

    h-index

1–5 of 571

Venues

  1. Frontiers in Neuroscience

    16 publications · 3.1K citations

    16

    h-index

  2. arXiv (Cornell University)

    Repository · 8 publications · 358 citations

    8

    h-index

  3. 7

    h-index

  4. The Journal of Chemical Physics

    3 publications · 433 citations

    3

    h-index

  5. Scientific Reports

    2 publications · 191 citations

    2

    h-index

1–5