Theory + Practice
I am starting up my own research program as a visiting assistant professor of computational neuroscience at Pomona College this fall (2023). If you are a student interested in working with me, please reach me through the means on my faculty page.
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:
1. How do simulated networks of neurons
change during learning?
2. How are these changes impacted by
including biological features, such as
low spike rates, low connectivity,
asynchronous activity patterns, and
adaptation in the network models?
3. What ultimately differentiates an
from networks that do not or are
not processing information?
Zhu Y, Smith CMB, Tang M, Scherr F, MacLean JN. Task success in trained spiking neuronal network models coincides with emergence of cross-stimulus-modulated inhibition. bioRxiv 2023.08.29.555334; doi: https://doi.org/10.1101/2023.08.29.555334.
Bojanek K*, Zhu Y*, MacLean JN. (2020) Cyclic transitions between higher order motifs underlie sustained asynchronous spiking in sparse recurrent networks. PLOS Computational Biology 16(9): e1007409.
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.