Cerebellar Learning Rules

Cerebellar Learning Rules is a theoretical framework in computational neuroscience that specifies the synaptic plasticity mechanisms and algorithmic principles underlying the adaptive refinement of motor coordination and sensorimotor integration within cerebellar circuits.

51

Publications

2.4K

Citations

164

Authors

90

Institutions

Publications per year

2017–2026

13

Authors

164

  1. JP
    John Porrill

    University of Sheffield, United Kingdom

    6 publications · 346 citations

    6

    h-index

  2. PD
    Paul Dean

    University of Sheffield, United Kingdom

    6 publications · 346 citations

    6

    h-index

  3. ED
    Egidio D’Angelo

    University of Pavia, Italy

    3 publications · 105 citations

    3

    h-index

  4. CI
    Chris I. De Zeeuw

    Erasmus University Rotterdam, Netherlands

    3 publications · 111 citations

    3

    h-index

  5. JC
    James C. Houk

    Northwestern University, United States

    3 publications · 162 citations

    3

    h-index

1–5 of 164

Venues

  1. Biological Cybernetics

    3 publications · 131 citations

    3

    h-index

  2. Current Biology

    2 publications · 146 citations

    2

    h-index

  3. Journal of Neuroscience

    2 publications · 269 citations

    2

    h-index

  4. Proceedings of the National Academy of Sciences

    PNAS · 2 publications · 100 citations

    2

    h-index

  5. PLoS Computational Biology

    2 publications · 47 citations

    2

    h-index

1–5