I am a postdoctoral researcher at the Harvard John A. Paulson School of Engineering and Applied Sciences. I am interested in a broad spectrum of questions in computational and theoretical neuroscience. My research aims to elucidate the principles underlying neural computation and cognition. Specifically, my research addresses questions like: 1) how does the brain efficiently sense and represent external world? 2) How does the brain organize and update memories to support adaptive behavior and life-long learning? 3)How does the brain solve computational problems at the algorithmic level? Answers to these questions will have broad implications, such as shedding light on treating neurodegenerative diseases and psychiatric disorders. I earned my PhD in condensed matter physics from Peking University. During my PhD, I explored various problems at the interface between physics and biology. I was advised by Dr. Chao Tang and Dr. Yuhai Tu at Center for Quantitative Biology. Before that, I studied physics at Central China Normal University at Wuhan. I also enjoy badminton, swimming, hiking, cooking, Chinese calligraphy, watching movies and reading.
PhD in Condensed Matter Physics, 2019
Peking University, Beijing, China
BS in physics, 2012
Central China Normal University, Wuhan, China
Animal exhibit distinct behavior that dependent their internal states even for the same sensory input. Drosophila larva responds different to odors when starved compared with fed. This internal state dependent oflactory behavior is controled by a topdown feedback to antennal lobe circuit.
Biologically plausible networks models with noisy synaptic update explain the widely observed representational drift.
We extend previous theory to investigate how the interplay of extrinsic noise and intrinsic noise affects early-warning signals near critical transitions.
Multineuronal imaging of the odor responses in C. elegans and decoding analysis revealed a distinct organization compared with insects and mamammlians.
To achieve efficient coding of odor information in an array of nonlinear olfactory receptors, the odor-receptor sensitivity matrix must be sparse. This sparsity depends on the statistics of environmental odors. We used analytical calclation and extensive numerical simulation to study the optimal sensitivity matrix for recptors with and without spontaneous (background) activity.
Biologically plausible networks models with noisy synaptic update explain the widely observed representational drift.
We built a circuit model of the first olfactory information processing center of fruit fly, which incorporates key features of neuron-neuron interactions such as short-term plasticity and presynaptic inhibition.
Research projects:
I was advised by Dr. Chao Tang at the Center for Quantitative Biology, Peking University. My research was focused on quantitative and systems biology, biophysics and computational neuroscience. Research projects:
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