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
Parents
41
Rational metareasoning and the plasticity of cognitive control
Falk Lieder, Amitai Shenhav, Sebastian Musslick et al. · PLoS Computational Biology · 2018 · 149 citations · Full text
Motor Learning Enhances Use-Dependent Plasticity
Firas Mawase, Shintaro Uehara, Amy J. Bastian et al. · Journal of Neuroscience · 2017 · 143 citations · Full text
Medicine, Neurobiology, Neuroscience +15
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University of California, Berkeley
Berkeley, United States
3 h-index
6 publications
581 citations
Princeton University
Princeton, United States
3 h-index
3 publications
280 citations
University of Bern
Bern, Switzerland
2 h-index
6 publications
333 citations
Yale University
New Haven, United States
2 h-index
4 publications
399 citations
The University of Tokyo
Tokyo, Japan
2 h-index
14 publications
490 citations

Ghent University
Ghent, Belgium
2 h-index
6 publications
334 citations
University of Sheffield
Sheffield, United Kingdom
2 h-index
5 publications
332 citations
Brown Institute for Media Innovation
New York, United States
2 h-index
2 publications
171 citations
1–8 of 101
6 publications · 486 citations
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4 publications · 485 citations
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3 publications · 249 citations
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3 publications · 187 citations
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2 publications · 140 citations
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