About me

My name is Yudi Xie (谢禹), a Ph.D. student at the Department of Brain and Cognitive Sciences, MIT. I work in Prof. Robert Guangyu Yang’s lab on building multi-systems neural network models of neural and cognitive processes. Before coming to MIT, I received my Bachelor’s degree in physics from the University of Science and Technology of China (USTC). During undergrad, I was very fortunate to have worked in Prof. Naoshige Uchida’s lab at Harvard University to investigate the role of dopamine in novelty exploration in mice. I also had a great time working in Prof. Quan Wen’s lab at USTC, investigating the C. elegans movement decisions.

For more information about me, check out my CV here.

Research interests

My main research interest is learning. How do humans and animals learn concepts, associations, skills, and knowledge? What algorithms do they use? How do neurons implement these algorithms? What representations are essential to these algorithms, and how are they learned or evolved? My research is motivated by these questions.

I am passionate about using machine learning, especially deep learning, as a tool and modeling framework to understand learning in animals and humans. I believe an essential part of understanding the brain and mind is to build a functional equivalent computational model. Although I like engineering, I want my research to be driven by scientific questions.

In a broader context, I am interested in exploring ways to combine machine learning techniques and insights from cognitive science to help people learn more efficiently in real-world educational applications.

On-going work

Multi-systems neural network models of working memory

Recorded talk at The 2nd Neural Network New Year Conference (Dec 31, 2022, talk in Chinese):

Human-like capacity limits in working memory models result from naturalistic sensory constraints

Check out our published abstract:

Xie, Y., Duan, Y., Cheng, A., Jiang. P., Cueva, C. and Yang, G.R. (2022). Human-like capacity limitation in multi-system models of working memory. 2022 Conference on Cognitive Computational Neuroscience


Dopamine and novelty exploration

Akiti, K., Tsutsui-Kimura, I., Xie, Y., Mathis, A., Markowitz, J.E., Anyoha, R., Datta, S.R., Mathis, M.W., Uchida, N. and Watabe-Uchida, M. (2022). Striatal dopamine explains novelty-induced behavioral dynamics and individual variability in threat prediction. Neuron

A news article about this work: Approach, engage, or avoid? How mice react to strange objects

C. elegans motor sequence generation

Wang, Y., Zhang, X., Xin, Q., Hung, W., Florman, J., Huo, J., Xu, T., Xie, Y., Alkema, M.J., Zhen, M. and Wen, Q. (2020). Flexible motor sequence generation during stereotyped escape responses. Elife, 9, p.e56942.