Ghost in the Machine: Melding Brain, Computer and Behavior

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Implantable devices are playing a greater role in neurologic care, but their effectiveness is limited, because they are blind to human thoughts, feelings, and behavior – factors that most dramatically affect our health. Coupling peripheral sensors to implants might help, but wouldn’t it be easier if the devices just asked us? Armed with this knowledge, next generation machines will more effectively drive neural activity in the brain to healthy states. They will also quickly learn behaviors that worsen health and guide us to better choices. Though DARPA, the NIH, and Neuralink are spending millions of dollars on new hardware for brain-computer interfaces, none focus on reciprocal, natural communication between host and machine. There is a desperate need for novel, practical methods that enable devices to learn from and guide human behavior. In this application I propose to develop a new generation of autonomous brain-machine interfaces – devices that can question, record, act - and combine learning algorithms applied to neurosignals with teaching by their human hosts. Life with these implants will entail a subtle human- machine dialogue in which devices and humans teach and learn from each other. Humans will inform intelligent algorithms about what we are doing and feeling, while machines will incorporate this information into therapy and guide us to optimize quality of life in personalized ways. This is a paradigm shift from today’s simple devices, which are programmed by physicians during occasional office visits. I propose to demonstrate this paradigm in a practical, scalable way using current epilepsy implants that is rapidly translatable to many neurological disorders. To achieve this goal, I will meld several cutting-edge technologies in novel ways, including: (1) State-of-the-art, high bandwidth implantables that sample neural activity, link to vast cloud- based computational power to process it, and intervene to modulate brain, spinal cord or peripheral neural activity. This work utilizes my experience from the past 20 years; (2) I will deploy powerful new computer science tools in novel ways. I will use convolutional neural nets (a.k.a. Deep Learning) to learn patterns from vast streams of continuous high-bandwidth neural data, build a two way human-machine interface using Natural Language Processing (NLP)., and probe networks with changes in human behavior and electrical stimulation and guide interventions toward therapeutic goals using Reinforcement Learning. Combining these computer science, machine learning techniques and measurements of human behavior is a new area of investigation for me that will leverage my unique background in clinical neurology and engineering to build a new class of interactive, human therapeutic devices.