Reinforcement Learning Plasticity

Reinforcement Learning Plasticity is a computational framework within learning-related neuroplasticity that specifically characterizes the dynamic adaptation of synaptic efficacy in neural circuits through reward-driven feedback signals to optimize goal-directed behavior.

41

Publications

2.5K

Citations

149

Authors

101

Institutions

Publications per year

2017–2026

22

Authors

149

  1. FL
    Falk Lieder

    University of California, Berkeley, United States

    3 publications · 193 citations

    3

    h-index

  2. AS
    Amitai Shenhav

    Brown University, United States

    2 publications · 171 citations

    2

    h-index

  3. TV
    Tom Verguts

    Ghent University, Belgium

    2 publications · 111 citations

    2

    h-index

  4. JF
    Johannes Friedrich

    Columbia University, United States

    2 publications · 111 citations

    2

    h-index

  5. SM
    Sebastian Musslick

    Brown University, United States

    2 publications · 171 citations

    2

    h-index

1–5 of 149

Venues

  1. Neural Computation

    6 publications · 486 citations

    6

    h-index

  2. PLoS Computational Biology

    4 publications · 485 citations

    4

    h-index

  3. Journal of Neuroscience

    3 publications · 249 citations

    3

    h-index

  4. eLife

    3 publications · 187 citations

    3

    h-index

  5. Frontiers in Behavioral Neuroscience

    2 publications · 140 citations

    2

    h-index

1–5