Laboratory for Computational Neuroscience
Research Projects
Finger Coordination
Co-Investigators: Dr. Chung Tin, Dr. Guanglie Zhang The goal of this project is to investigate the neural bases of the coordination of fingers. A hierarchical modeling approach, describing the general information transformation from neural signals, spinal modules, to muscle activities related to finger movements in healthy subjects, will offer a more reliable model structure for the prediction of finger kinematics from neural recordings, which will potentially be beneficial to mobile motor prosthetic applications. Another important objective of this proposed study is to investigate the changes in neural modules, muscle synergies, and nonlinear dynamics of neuromuscular system associated with motor learning.

Changes in Neural Dynamics During Learning and Development
We are currently developing novel modeling methodology to identify the time-varying properties of nonlinear neural dynamics by observing neural spike train inputs and outputs only. The project objective is to estimate the time-varying nonlinear dynamic models of local neural circuits during development and learning using the new electrophysiological recordings. These data-driven models aim to provide valuable information to the development of treatments, and neural prosthetic devices to address neurologic diseases.

Novel EEG Applications
Collaborators: Dr. Joe Mak (NeuroSky), Dr. Savio Wong (HKIEd) This is a collaborative project with an Electroencephalography (EEG) headset manufacturer, NeuroSky, and the Hong Kong Institute of Education. This system will provide an innovative approach for integrating brain activity in the interaction with environment, which will bring radical changes in education and entertainment business (e.g. gaming and interactive media).

Light-Induced Locomotor Response of Zebrafish During Early Development
Collaborators: Dr. Yuk Fai Leung (Purdue), Professor Tommy Chow (CityU) The goal of this project is to model the light-induced locomotor response of zebrafish during early development. Through profiling visual-motor response of zebrafish of different genotypes, how certain factors affect behavioral responses can be investigated. We are developing computational techniques to utilize the potential of this behavioral assay for high-throughput drug screening. This project utilizes advanced mathematical modeling and data mining techniques to accurately classify different types of zebrafish based on their behavioral data.

[1] ZebraBox Image Reference: