ORCID iD: 0000-0002-9232-1420
Scopus Author ID: 55712228200
Google Scholar Profile: yTEnHWkAAAAJ

Metrics for Academic and Research Impact

Prof. Liu graduated from Beijing Medical University (currently Peking University Health Science Center) with a B.M. degree in Basic Medical Science. He also obtained an M.Sc. degree in the Program in Neuroscience from the University of Toronto, where he was mentored by Prof. Xian-min Yu at the Center for Addiction and Mental Health (CAMH). Prof. Liu received his postdoctoral training under the supervision of Prof. Zhao-wen Wang at the University of Connecticut Health Center (UCHC) and Prof. Erik Jorgensen at the University of Utah and Howard Hughes Medical Institute (HHMI). Prior to joining the Department of Neuroscience at the City University of Hong Kong in late 2021, Prof. Liu served as a Research Assistant Professor in the Laboratory of Cori Bargmann at Rockefeller University from 2013 to 2021. Throughout his career, Prof. Liu has received several notable awards and recognitions, including the Grass Fellowship from the Grass Foundation in 2010, a two-time recipient of the Kavli Neural Systems Institute pilot grants from the Kavli Foundation in 2017 and 2020, the Collaborative Research in Computational Neuroscience (CRCNS) Award from the National Science Foundation (USA) in 2021, and the Early Career Award from the Research Grants Council (RGC) of Hong Kong in 2022.

Research Interests

The integrated function of the human brain allows every individual human to have unique thoughts, perceptions, memories, and actions. These complex abilities arise from the interconnected neurons in the brain, which acquire information about the world, integrate it with ongoing knowledge and motivational states, and drive subsequent decisions and actions. Mechanistically understanding how our brain accomplishes these incredibly sophisticated functions, or even simulating our brain on a computer one day, is one of the grand challenges of our time. This is a daunting task that requires a comprehensive understanding of the brain at every level of complexity, from molecules to neurons, circuits, systems, and the underlying computational principles.

Compared to the human brain with approximately 86 billion neurons and 100 trillion synapses, the brain of the nematode worm Caenorhabditis elegans has only 302 neurons and several thousand synapses. To achieve the ultimate goal of understanding our brain, we must first comprehend and model much simpler brains. At the scale of C. elegans, scientists have been able to map the physical wiring of the entire nervous system, known as the connectome, in an attempt to reconstruct the worm brain. However, it soon became clear that structure alone did not explain function. Without the knowledge of the cell-type-specific biophysical properties of individual neurons and the activity patterns they produce, theorists were unable to generate a unifying model that explained how the seemingly "simple" worm brain works. The Liu lab aims to address this problem by comprehensively characterizing the biophysical properties of every neuronal cell type in C. elegans and constructing highly constrained models at the single-neuron and circuit levels. The long-term goal of the Liu lab is to biophysically map the entire worm brain, reproduce neural activity patterns in different neuron types and neural circuits, and ultimately simulate how the worm brain generates behaviors.

Specifically, the research of the Liu lab is focuses on the following three fronts:

• Systematically recording from every neuron type in C. elegans using electrophysiology to establish a complete biophysical atlas of the worm brain.
• Exploring the functional significance of diverse biophysical properties in cellular and circuit physiology, neural computation, and animal behavior.
• Constructing conductance-based single-neuron models, as well as anatomically correct and biophysically accurate network models, to simulate the C. elegans nervous system.

We are seeking talented PhD students, Research Assistants, and Postdocs to join our team. Interested candidates please contact [email protected].