About me

My name is Yudi Xie (谢禹), and I am currently pursuing my Ph.D. in Prof. James DiCarlo’s lab in the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology (MIT). Before embarking on this journey at MIT, I obtained my Bachelor’s degree in Physics from the University of Science and Technology of China (USTC).

My main research interest is at the intersection of computational neuroscience, cognitive science, and machine learning. Through the lens of machine learning, I utilize deep learning and probabilistic models to develop a computational framework for understanding the intricacies of the brain and mind. These computational models are then tested against empirical behavioral and neural data.

During my undergraduate years, I had the exceptional opportunity to do research in Prof. Naoshige Uchida’s lab at Harvard University. There, I used computational tools to investigate the influence of dopamine on novelty-seeking behaviors in mice. My interest in computational neuroscience research was initially sparked through my experience in Prof. Quan Wen’s lab at USTC, where I investigated the movement decisions of C. elegans.

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

Publications

Sensory-cognitive neural network models of working memory

Xie, Y.*, Duan, Y.*, Cheng, A., Jiang. P., Cueva, C. and Yang, G.R. (2023). Natural constraints explain working memory capacity limitations in sensory-cognitive models. bioRxiv 2023.03.30.534982.

Conference abstracts and talks about this work:

Talk: Working Memory Symposium (June 2023). Natural constraints explain working memory capacity limitations in sensory-cognitive models (talk start at 32:00)

Abstract: Computational and Systems Neuroscience (COSYNE 2023). Human-like capacity limits in working memory models result from naturalistic sensory constraints.

Talk: Neural Network New Year Conference (Dec 31, 2022, the talk is in Chinese). Human-like capacity limits in working memory models result from naturalistic sensory constraints.

Abstract: Conference on Cognitive Computational Neuroscience (CCN 2022). Human-like capacity limitation in multi-system models of working memory.

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, 110(22), pp.3789-3804.

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.

Blogs

In my free time, I write about my thoughts on research or other things I enjoy in life.

如何对待负面结果 读博前我希望就知道的事 (一) (Sep. 2023)

What Piano Taught Me About Learning (Jun. 2021)

申请国外PhD项目,你需要注意什么? (Jan. 2021)

Where Computing Meets Brain Research - My first impression of the BCS Department at MIT (Jan. 2020)