Title：On Cognitive Neural Prosthesis: Nonlinear Dynamic Modeling and VLSI Implementation      认知神经假体：非线性动态建模和VLSI实现 Speaker：Dr. Ray C. C. Cheung, City Univ. of Hong Kong         香港城市大学副教授张泽松博士 时间：2015年12月24日（周四）上午9:00 地点：玉泉校区信电楼117

Biography:

Dr. Ray C.C. Cheung received his BEng and MPhil degree in computing engineering from CUHK in 1999 and 2001, and received his Ph.D. degree in computing from Imperial College London (IC) in 2007. He received the Hong Kong Croucher Foundation Fellowship and Scholarship for his postdoctoral and doctoral research work at UCLA and IC. Currently, he is an associate professor (tenured) in the Department of Electronic Engineering, City University of Hong Kong (CityU). He is the author of more than 40 journal papers and over 50 conference papers. His current research interests include cryptographic hardware designs, rapid prototyping trusted computing platforms, high-performance biomedical VLSI designs, and mobile application developments. He is currently the research group leader of the CityU Architecture Lab of Arithmetic and Security (CALAS). He is currently the Director of CityU Apps Lab (CAL) supporting Campuswide Mobile Application development. He is the associate program leader, Computer and Data Engineering, Department of Electronic Engineering, City University of Hong Kong. He received the CityU Teaching Excellence Award in 2012.

Abstract:

Neural information is represented and transmitted among neuronal units by a series of all-or-none “neural codes”. During the process of neural prosthesis design, generally, a large amount of “neural codes” need to be captured and analyzed, which brings about an important discipline, known as neuroinformatics. However, in neuroinformatics study, this coding process, also termed as “spiking activity”, is not straightforward for prediction. It owes to the high nonlinearity and dynamic property involved in generation of the neuronal spikes. In this talk, a novel generalized Volterra kernel-based neural spiking activity simulator is introduced for prediction of the neural codes in mammalian hippocampal region. High-performance VLSI architecture is established for the simulator based on high-order Volterra kernels involving cross terms. The effectiveness and efficiency of the simulator are proven in experimental settings. This simulator has the potential to serve as a core functional unit in future hippocampal cognitive neural prosthesis.