Affilliation: Johns Hopkins University, USA
Ralph Etienne-Cummings, an IEEE and NAS Kavli Frontiers Fellow, received his B.Sc. in physics, 1988, from Lincoln University, Pennsylvania. He completed his M.S.E.E. and Ph.D. in electrical engineering at the University of Pennsylvania in December 1991 and 1994, respectively. Currently, Dr. Etienne-Cummings is a professor of electrical and computer engineering, and computer science at Johns Hopkins University (JHU). He is the current Chairman of the Electrical and Computer Engineering Department at JHU. He was the founding Director of the Institute of Neuromorphic Engineering (INE), and currently serves Treasurer of the INE. He was also the Associate Director for Education and Outreach of the National Science Foundation (NSF) sponsored Engineering Research Centers on Computer Integrated Surgical Systems and Technology at JHU. He has served as Chairman of the IEEE Circuits and Systems (CAS) Technical Committee on Sensory Systems and on Neural Systems and Application, and was re-elected as a member of CAS Board of Governors from 1/2007 – 1/2009. He was also the General Chair of the IEEE BioCAS Conference in 2008, and serves on its Steering Committee. He is the Co-General Chair of IEEE ISCAS 2017, which will be held in Baltimore, MD. He was also a member of Imagers, MEMS, Medical and Displays Technical Committee of the ISSCC Conference from 1999 – 2006. He also serves on numerous editorial boards and was the Deputy Editor in Chief for the IEEE Transactions on Biomedical Circuits and Systems. He is the recipient of the NSF’s Career and Office of Naval Research Young Investigator Program Awards. In 2006, he was named a Visiting African Fellow and a Fulbright Fellowship Grantee for his sabbatical at University of Cape Town, South Africa. He was invited to be a lecturer at the National Academies of Science Kavli Frontiers Program, held in November 2007. He won the 2010 JHU Applied Physics Lab R.W. Hart Prize for Best R&D Project in Development. He has also won publication awards, including the 2012 Most Outstanding Paper of the IEEE Transaction on Neural Systems and Rehabilitation Engineering, 2011 Best Paper Award for IEEE Transactions of Biomedical Circuits and Systems, 2003 Best Paper Award of the EURASIP Journal of Applied Signal Processing, “Best Demonstration” at the IEEE BioCAS 2012, and “Best Ph.D. in a Nutshell” at the IEEE BioCAS 2008 Conference, and has been recognized for his activities in promoting the participation of women and minorities in science, technology, engineering and mathematic. He was also recently recognized as a “ScienceMaker”, as part of the HistoryMakers which is an African American history archive. His research interest includes mixed signal VLSI systems, computational sensors, computer vision, neuromorphic engineering, smart structures, mobile robotics, legged locomotion and neuroprosthetic devices. He has published more than 230 technical articles, 1 book, 12 book chapters and holds numerous patents on his work in these subjects.
Talk Title: I, Robot: Blurring the lines between Mind, Body and Robotics
It is postulated that future brain-machine interfaces (BMI) will not only be used to infer the host’s intentions from brain activities (e.g. motor decode) or to provide the host with extrinsic information (e.g. sensory feedback), but may provide extensions, enhancements and replacement of parts of the brain itself. For example, the BMI implant may replace injured parts of the brain after traumatic brain injuries (TBI), extend the host’s memory with a hippocampus implant, provide enhanced visual capabilities by allowing the host to see different parts of the optical spectrum, or provide bat-like echolocation capabilities to blind humans. These applications of BMI are clearly in the realm of science fiction at this point and time, however, progress is being made to model and mimic parts of the brain so that we are getting closer to making these dreams a reality. We assert that such BMI devices will not be truly realized until the electronics “speak the same language” as the biological brain. That is, the signaling interface between the electronic and biological systems must match and the computation performed by the electronic device must also be implemented similarly to biological computation. Hence, the complete system must be neuromorphic, implying that it must replicate both the form and function of the brain. This is a departure from the current approach in the BMI community, which wraps translational circuitry (neural amplifies and biphasic current stimulators) around traditional digital CPU based processing when interfacing with the nervous system. Our approach will still make use of the interface circuits, however, the computation will be performed with analogs of neurons, synapses, axons and dendrites in silicon. As new methods for interfacing to the nervous system emerge (i.e. carbon nano-tubes, memristers and optogenetics systems), the interface circuits will also be replaced with more biologically plausible versions. This talk summarizes the computation inside future neural prosthesis systems that take the neuromorphic engineering approach.