Theory + Practice

a beginning

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.

I am currently asking:

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?

Publications:

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

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.

Previously:

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

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 and 

computational models of cognition.