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. (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 (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.
