YUQING ZHU

I am a PhD candidate in the lab of Jason MacLean at the University of Chicago, and a 5th-year student in the computational neuroscience program. My research combines my fascination with the visual system with my skills in mathematical modeling. I also have a healthy interest in the collusion between neuroscience and artificial intelligence.

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I am currently asking:

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How do simulated networks of neurons change during learning? How are these changes impacted by biological features, such as including low spike rates, low connectivity, and adaptation in the simulations?

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Publications:

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Bojanek K*, Zhu Y*, MacLean J. (2020) Cyclic transitions between higher order motifs underlie sustained asynchronous spiking in sparse recurrent networks. PLOS Computational Biology 16(9): e1007409. 

https://doi.org/10.1371/journal.pcbi.1007409

*co-first-authors

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In this study we ask how an extremely sensitive and complex system, such as a biological brain, maintains stable activity across time. We algorithmically build spiking neural network models with neocortical dynamics. Using a combination of graph theory, information theory, and probabilistic tools, we explore the mechanisms of sustained activity in network simulations. The focus of our analysis is on multi-neuron interactions that manifest in the network through time.

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Previously:

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In my first year in the PhD program at UChicago, I rotated through four labs, dabbling in machine learning, connectomics, and behavior in rhesus macaque monkeys and mice. 

 

Prior to enrolling at UChicago, I have been a research assistant in the labs of:

 

I. Josef Parvizi at Stanford Medicine,
where I studied human visual perception of symbols using electrocorticography

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II. Marina Bedny at Johns Hopkins University,

where I studied the development of human mathematical cognition
 

III. Chung-Chuan Lo at National Tsing Hua University, where I used a Drosophila full-brain simulation to survey the effects of synaptic long-term potentiation (LTP) on signal propagation and visual learning.

 

IV. Rong Xue at the Chinese Academy of Sciences, where I probed the limits of diffusion tensor imaging (DTI) in tracing human sensory pathways.

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I completed my B.S. in neuroscience and cognitive science at Johns Hopkins University in Dec 2016. My focal areas were sensory systems and 

computational models of cognition.