Associative Synaptic Learning

Associative Synaptic Learning is a theoretical construct in neuroscience that posits synapses as dynamic computational elements whose strength changes in response to correlated pre- and postsynaptic activity, thereby enabling neurons to associate temporally contiguous inputs through Hebbian plasticity mechanisms.

133

Publications

8.2K

Citations

631

Authors

258

Institutions

Publications per year

2017–2026

47

Authors

631

  1. DL
    Daniel L. Alkon

    National Institutes of Health, United States

    7 publications · 507 citations

    7

    h-index

  2. ET
    Edmund T. Rolls

    University of Oxford, United Kingdom

    5 publications · 503 citations

    5

    h-index

  3. BT
    Bobo Tian

    East China Normal University, China

    4 publications · 159 citations

    4

    h-index

  4. AK
    Andreas Knoblauch

    Honda (Germany), Germany

    4 publications · 63 citations

    4

    h-index

  5. SM
    Simon M. Stringer

    University of Oxford, United Kingdom

    4 publications · 329 citations

    4

    h-index

1–5 of 631

Venues

  1. Journal of Neuroscience

    7 publications · 454 citations

    7

    h-index

  2. Network Computation in Neural Systems

    6 publications · 400 citations

    6

    h-index

  3. Proceedings of the National Academy of Sciences

    PNAS · 5 publications · 330 citations

    5

    h-index

  4. Brain Research

    4 publications · 100 citations

    4

    h-index

  5. Neural Computation

    4 publications · 94 citations

    4

    h-index

1–5