Tao Letian is a researcher whose main research focus is on using mathematical and large-scale scientific computing methods to simulate, numerically simulate, and analyze the dynamic properties of neuronal networks in neurobiology. Through mathematical analysis, he aims to understand how the biological functions in the mammalian cortex emerge from the interactions among different components of neuronal networks. One of his significant contributions in the field of neurobiology is revealing the possible connection between neuronal network dynamics and network structure, which has been supported by experimental evidence in neuroscience. By simulating large-scale neuronal networks in the mammalian visual cortex and using mathematical methods, he identified a possible bifurcation structure and determined a dynamic operating point in the visual cortex, uncovering a new mechanism for mammalian visual orientation selectivity.
Since returning to China, his research group has focused on the following:
Using data spatiotemporal decomposition methods to study the dynamic properties of large-scale neuronal networks and conducting data-driven dimensionality reduction.
Applying mathematical dimensionality reduction methods to analyze optical imaging data and developing algorithms for predicting neuronal functional networks and functional connectivity.
Conducting optical imaging techniques with high spatial and temporal resolutions, developing optical imaging systems for synchronized imaging of worm behavior and its neural circuits.
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