Understanding and engineering brain function has been identified as a grand scientific challenge in recognition of its potential to revolutionize a number of fields including computing and medicine. Fully-implantable Bidirectional Brain Computer Interfaces (BBCI) are the focus of this project. These are electronic systems capable of recording, processing and stimulating neural activity, and they play a foundational role in enabling better understanding of the brain. Implantable BBCIs will enable neuro-scientists to explore brain function in unprecedented detail, and help realize neuro-prosthetic devices capable of restoring mobility, vision and brain function among the disabled. However, critical technological barriers facing BBCIs are hindering progress in neuroscience. Existing BBCI architectures do not scale well, in terms of power or area, to support ever-increasing numbers of recording and stimulation channels needed for finer examination and control of brain function. Furthermore, neural stimulation produces artifacts - electrical disturbances in the brain - that hamper the ability to perform neural recording. Finally, the desired level of computational performance required to perform neural signal processing and data-communication to external devices consumes power in excess of thermal limits of implantable devices. The technologies resulting from this project will be translated into a fully implantable, bio-compatible and versatile closed-loop neuroscience platform that overcomes these existing challenges. Collaborations with neuroscientists, the medical-device industry, and fabrication partners, to be pursued during this project, are critical to the realization of this goal. The resulting platform will be made available to the broader neuroscience community to enable experiments at unprecedented levels of scale and scope, accelerating progress toward understanding the brain. Both graduate and underrepresented minority students will be involved in the project.
This award addresses the critical BBCI barriers of power, area, performance and recording quality by investigating and devising cross-cutting technologies that span digital/mixed-signal circuit design, architecture, systems theory and system integration. Exploiting domain-specific structure across every level of abstraction, from algorithm partitioning down to package- and circuit-design is central to this project. The effort is organized into three threads: 1) Development of novel, computationally-enhanced neural interfaces to achieve desired levels of efficiency and scalability; 2) Exploration of domain-correspondence to Multiple-Input Multiple Output (MIMO) communication systems through low-energy computing, which will allow systems capable of rejecting artifacts and allowing recording to be focused to a targeted set of neurons; and 3) Leveraging an understanding of neural signal processing algorithms and preliminary results in ultra-low power computing to devise domain specific architectures that meet BBCI processing requirements under severe power limitations.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.