Theory + Practice

a beginning

I am a PhD candidate in the lab of Jason MacLean at the University of Chicago, and a 3rd-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.

I am currently asking:

How do networks of neurons - both biological

and mathematical - change during learning?

How do these changes depend on the networks' unique and shared features?

Publications:

Bojanek K*, Zhu Y*, MacLean J. 2019. Cyclic transitions between higher order motifs underlie sustained activity in asynchronous sparse recurrent networks.

*co-first-authors.

Preprint on biorXiv.

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 (SNNs) 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 beyond-pairwise interactions, especially triangle motifs, that manifest in the network through time.

Previously:

In my first year in the PhD program at UChicago, I rotated through four labs, dabbling in machine learning, connectomics, and training 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

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.

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

computational models of cognition, and philosophy of mind.

© 2020

Yuqing Zhu