Input-output Transformation

Input-Output Transformation is a computational framework in neuroscience that characterizes how a neuron integrates synaptic inputs across its dendritic tree to generate a specific pattern of action potential output, distinguishing it from simpler activation models by explicitly accounting for the spatiotemporal dynamics and nonlinear interactions of dendritic processing.

179

Publications

8.6K

Citations

508

Authors

275

Institutions

Publications per year

2017–2026

61

Authors

508

  1. PC
    Pin‐Yu Chen

    IBM (United States), United States

    3 publications · 124 citations

    3

    h-index

  2. JK
    Judit K. Makara

    HUN-REN Institute of Experimental Medicine, Hungary

    3 publications · 164 citations

    3

    h-index

  3. BV
    Branislav Vlahović

    North Carolina Central University, United States

    2 publications · 24 citations

    2

    h-index

  4. MW
    Maolin Wang

    Sichuan University, China

    2 publications · 122 citations

    2

    h-index

  5. CL
    Chun‐An Lu

    Industrial Technology Research Institute, Taiwan

    2 publications · 24 citations

    2

    h-index

1–5 of 508

Venues

  1. arXiv (Cornell University)

    Repository · 12 publications · 476 citations

    12

    h-index

  2. International Journal of Control

    6 publications · 290 citations

    6

    h-index

  3. IEEE Transactions on Automatic Control

    4 publications · 109 citations

    4

    h-index

  4. Journal of Neurophysiology

    JN · 3 publications · 86 citations

    3

    h-index

  5. IFAC Proceedings Volumes

    3 publications · 54 citations

    3

    h-index

1–5