Reinforcement Learning Circuits

Reinforcement Learning Circuits is a neurobiological framework that integrates decision-making and neuromodulatory circuits to mechanistically explain how agents learn optimal behavioral policies through the dynamic interaction of synaptic plasticity, reward prediction errors, and dopaminergic signaling.

72

Publications

5.2K

Citations

319

Authors

153

Institutions

Publications per year

2017–2026

44

Authors

319

  1. SM
    Stephanie M. Groman

    Yale University, United States

    3 publications · 199 citations

    3

    h-index

  2. JY
    Juliet Y. Davidow

    Harvard University, United States

    2 publications · 128 citations

    2

    h-index

  3. RG
    Radu Grosu

    TU Wien, Austria

    2 publications · 294 citations

    2

    h-index

  4. IB
    Ilana B. Witten

    Princeton University, United States

    2 publications · 64 citations

    2

    h-index

  5. ML
    Mathias Lechner

    Institute of Science and Technology Austria, Austria

    2 publications · 294 citations

    2

    h-index

1–5 of 319

Venues

  1. Journal of Neuroscience

    7 publications · 645 citations

    7

    h-index

  2. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 5 publications · 153 citations

    5

    h-index

  3. eLife

    4 publications · 148 citations

    4

    h-index

  4. NeuroImage

    4 publications · 193 citations

    4

    h-index

  5. Nature Communications

    3 publications · 228 citations

    3

    h-index

1–5