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
72
Neural circuit policies enabling auditable autonomy
Mathias Lechner, Ramin Hasani, Alexander Amini et al. · Nature Machine Intelligence · 2020 · 282 citations
Spontaneous behaviour is structured by reinforcement without explicit reward
Jeffrey E. Markowitz, Winthrop F. Gillis, Maya Jay et al. · Nature · 2023 · 225 citations · Full text
1–5 of 72
319
3
h-index
2
h-index
2
h-index
2
h-index
2
h-index
1–5 of 319
153
Harvard University
Cambridge, United States
8 h-index
37 publications
4.7K citations

Allen Institute for Brain Science
Seattle, United States
5 h-index
6 publications
292 citations
New York University
New York, United States
4 h-index
4 publications
716 citations
Emory University
Atlanta, United States
4 h-index
9 publications
646 citations
University of Pennsylvania
Philadelphia, United States
4 h-index
5 publications
226 citations
University of California, Los Angeles
Los Angeles, United States
4 h-index
10 publications
893 citations
National Institutes of Health
Bethesda, United States
3 h-index
13 publications
1.3K citations
Brown University
Providence, United States
3 h-index
5 publications
607 citations
1–8 of 153
7 publications · 645 citations
7
h-index
Repository · 5 publications · 153 citations
5
h-index
4 publications · 148 citations
4
h-index
4 publications · 193 citations
4
h-index
3 publications · 228 citations
3
h-index
1–5