Correlation-based Plasticity

Correlation-Based Plasticity is a theoretical framework within Hebbian plasticity that posits synaptic strength changes as a function of the statistical correlation between pre- and postsynaptic neural activities, distinguishing itself from other plasticity models by emphasizing covariance-driven learning rules over temporally precise spike-order dependence.

54

Publications

4.3K

Citations

162

Authors

101

Institutions

Publications per year

2017–2026

13

Authors

162

  1. SR
    Stefan Rotter

    University of Freiburg, Germany

    3 publications · 511 citations

    3

    h-index

  2. PK
    Peter König

    Osnabrück University, Germany

    2 publications · 123 citations

    2

    h-index

  3. MG
    Matthieu Gilson

    Universitat Pompeu Fabra, Spain

    2 publications · 134 citations

    2

    h-index

  4. DJ
    Daniel J. Amit

    Hebrew University of Jerusalem, Israel

    2 publications · 87 citations

    2

    h-index

  5. FW
    Florentin Wörgötter

    University of Göttingen, Germany

    2 publications · 91 citations

    2

    h-index

1–5 of 162

Venues

  1. Neural Computation

    6 publications · 700 citations

    6

    h-index

  2. Biological Cybernetics

    4 publications · 196 citations

    4

    h-index

  3. PLoS Computational Biology

    3 publications · 109 citations

    3

    h-index

  4. Journal of Neuroscience

    3 publications · 1.2K citations

    3

    h-index

  5. Neurocomputing

    3 publications · 68 citations

    3

    h-index

1–5