Title：Pushing physical limits in intracortical neural recordings

Invited Speaker：Dr. Chong Xie

            Department of Biomedical Engineering     

            The University of Texas at Austin, USA

Venue：澳门新甫京娱乐娱城平台综合科研楼137会议室              

Time：10:00 am, May 29, 2018

Abstract：

The brain is a massively-interconnected and constantly-evolving network of specialized circuits, a systematic understanding of which requires an interface that functions at diverse spatial and temporal scales. Implanted electrodes provide a unique approach to decipher brain circuitry by allowing for time-resolved electrical detection of individual neuron activity. However, scalable and stable neural recording that can track and map a large ensemble of neurons across days, weeks and months remains challenging. We recently demonstrated that ultraflexible, cellular-dimensioned neural electrodes afford seamless integration with brain tissue and reliable recording of individual neurons for over a year. Building upon this platform, I will further describe our recent progress in reliable detection and isolation of individual units, as well as functional mapping and chronic tracking of the local circuitry in a neuronal cluster over several months in behaving brains. Finally, I will discuss our on-going efforts in scaling up this technology.

Resume：

Dr. Chong Xie is at present an assistant professor at the Department of Biomedical Engineering, University of Texas at Austin, USA. He received his B.S. in University of Science and technology of China, and Ph. D. degree from Stanford University. After postdoctoral studies in Harvard University, he joined University of Texas at Austin in 2014. Currently, the Xie group is mainly interested in applying advanced nanoelectronic devices to various neural systems. Ongoing projects include high-density neural probes for brain activity mapping, more biocompatible neural probes for chronically stable brain-machine interface, and 3D neuronal culture - nanoelectronics hybrid for an in vitro 'brain-like' model system.