Speaker：Mingze Dong（董明泽）Lan Luo（罗兰）Sijie Lin（凌思捷）Sijie Liu（刘宇轩）Zimo Zhangren（张任子墨）

Time：10:00 - 13:00

Location：Room B106, Lui Che Woo Building

Abstract

Reinforcement learning is an adaptive process in which an animal utilizes its previous experience to improve the outcomes of future choices. Computational theories of reinforcement learning play a central role in the newly emerging areas of neuroscience. In this framework, actions are chosen according to their value functions, which describe how much future reward is expected from each action. Value functions can be adjusted not only through reward and penalty, but also by the animal’s knowledge of its current environment. Studies revealed that a large proportion of the brain is involved in representing and updating value functions and using them to choose an action, i.e., the dopamine system. We aim to summarize recent advances in the theory of reinforcement learning in the field of neuroscience, and various experimental evidence that link the learning process to neuronal response properties.

Guest information:

1. Lee, D., Seo, H., & Jung, M. W. (2012). Neural basis of reinforcement learning and decision making. Annual review of neuroscience, 35, 287-308.

Link: https://www.annualreviews.org/doi/full/10.1146/annurev-neuro-062111-150512

1. Niv, Y. (2009). Reinforcement learning in the brain. Journal of Mathematical Psychology, 53(3), 139-154.

Link: https://doi.org/10.1016/j.jmp.2008.12.005

1. Babayan, B. M., Uchida, N., & Gershman, S. J. (2018). Belief state representation in the dopamine system. Nature communications, 9(1), 1891.

Link: https://www.nature.com/articles/s41467-018-04397-0

2. Wang, J. X., Kurth-Nelson, Z., Kumaran, D., Tirumala, D., Soyer, H., Leibo, J. Z., ... & Botvinick, M. (2018). Prefrontal cortex as a meta-reinforcement learning system. Nature neuroscience, 21(6), 860.

Link: https://www.nature.com/articles/s41593-018-0147-8