Zeb Kurth-Nelson

• BS in Computer Science, 2003, Iowa State University
• PhD in Neuroscience, 2009, University of Minnesota

Research interests

• Deep RL models for decision making in the brain
• Exploration, experimentation, active learning
• Spontaneous sequences and learning and using relational maps
• Brain-inspired network architectures

Selected publications

Kurth-Nelson, Z., Economides, M., Dolan, R. J., & Dayan, P. (2016). Fast sequences of non-spatial state representations in humans. Neuron, 91(1), 194–204. doi:https://doi.org/10.1016/j.neuron.2016.05.028

Wang, J. X., Kurth-Nelson, Z., Kumaran, D., Tirumala, D., Soyer, H., Leibo, J. Z., Hassabis, D., & Botvinick, M. (2018). Prefrontal cortex as a meta-reinforcement learning system. Nature Neuroscience, 21, 860–868. doi:https://doi.org/10.1038/s41593-018-0147-8

Dasgupta, I., Wang, J., Chiappa, S., Mitrovic, J., Ortega, P., Raposo, D., Hughes, E., Battaglia, P., Botvinick, M., & Kurth-Nelson, Z. (2019). Causal reasoning from meta-reinforcement learning. arXiv, 1901.08162. https://arxiv.org/abs/1901.08162

Dabney, W., Kurth-Nelson, Z., Uchida, N., Starkweather, C. K., Hassabis, D., Munos, R., & Botvinick, M. (2020). A distributional code for value in dopamine-based reinforcement learning. Nature Neuroscience, 577, 671–675. doi:https://doi.org/10.1038/s41586-019-1924-6