Brain network architecture;
Predictive coding in brain;
I am currently seeking for master students, PhD students, Postdoctoral scholars at SUSTech, Shenzhen, China.
(only for academic uses)
Hi, I am Quanying Liu, a postdoctoral scholar in Computing and Mathematical Sciences at California Institute of Technology.
I am interested in understanding the computational processes in brain, the underlying neural basis. For instance, in order to respond to the changes of environment rapidly, our brain has an internal model at higher level to predict (and therefore prepare for) the future coming events, and to be flexibly updated based on the lower-level sensory input. At computational level, how the dynamic predictive process is computed in brain, and how would this process change with aging, or disease. At neural level, how the neural information is encoded to implement this internal model, and how it organized by the brain oscillations, e.g. delta, theta, alpha, beta and gamma waves, and how the neural information flows at network level, to support a variety of neurocomputational needs, and how to manipulate the neurocomputational process by means of modulating the neural activity. To these ends, I developed computational models and applied fMRI, high-density EEG and source imaging techniques.
In Oct 2017, I joined Dr. John Doyle's lab at Caltech where I will investigate the neural correlates of motor control and learning effects, and collaborated with Dr. Michael Harrington's group at HMRI, who has been exploring molecular or neuronal changes in pre-symptom Alzheimer's disease for two decades.
This website is updated by Quanying on July 26, 2019.