"Design of next-generation neural interfaces for decoding the brain"

Abstract

Neural interface aims to bridge the nervous system and outside world by recording and modulating neural signals. In the past decades, microelectrode array (MEA) technologies have contributed considerably to the acquisition of electrophysiological signals. Impressive progress has been made on developing MEAs with high-density, large throughput and ultra-flexibility. Nevertheless, there are still long-standing limitations in the current neural interfaces. For example, electrophysiology is blind to the cell types and has limited spatial resolution due to its electrical nature of recording. Meanwhile, mainstay neural MEAs are constrained to 2-dimensional (2D) interfacing with the brain, which mismatched with the 3D nature of the neural circuits. Apart from the MEA designs, connecting strategy is another important factor for successful neural recording. However, no reliable approach exists to efficiently connect large scale, soft arrays to recording electronics.

Herein, we propose next-generation neural interfaces, namely transparent microelectrode arrays and 3-dimensional (3D) neural probes, along with the device characterizations, novel connecting strategy as well as in vivo validations. Together, we envision these advanced neural interfaces will be broadly applied to neuroscience research and facilitate the understanding of the nervous system.

Thesis Committee

• Hui Fang, Chair
• Solomon Diamond
• John Zhang
• Tracy Cui (University of Pittsburgh)

Contact

For more information, contact Theresa Fuller at theresa.d.fuller@dartmouth.edu.