1. http://nrg.mbi.ufl.edu Symbiotic Brain-Machine Interfaces Justin C. Sanchez, Ph.D. Assistant Professor Neuroprosthetics Research Group (NRG) University of Florida http://nrg.mbi.ufl.edu jcs77@ufl.edu

2. http://nrg.mbi.ufl.edu Enabling Neurotechnologies for Overcoming Paralysis Develop direct neural interfaces to bypass injury. Communicate and control (closed- loop, real-time) directly via the interface. Leuthardt

3. http://nrg.mbi.ufl.edu Vision for BMI in Daily Life Lebedev

4. http://nrg.mbi.ufl.edu What are the Building Blocks? Signal Sensing Amplification Pre-Processing Telemetry Interpret Neural Activity Control Scheme Feedback Closed Loop BMI Provide neurophysiologic basis and engineering theory for a fully implantable neural Interface for restoring communication and control

5. http://nrg.mbi.ufl.edu BMI lessons learned Relationship between user and BMI is inherently lopsided. Users are intelligent and can use dynamic brain organization and specialization while BMIs are passive devices that enact commands I/O models have difficulty contending with new environments without retraining Laboratory BMIs need to be better prepared for ADL

6. http://nrg.mbi.ufl.edu Translating Thoughts into Action: The Neural Code Stimulus Neural System Neural Response StimulusNeural Response CodingGivenTo determine DecodingTo determineGiven

7. http://nrg.mbi.ufl.edu Vision for Next Generation Brain- Machine Interaction Intelligent behavior arises from the actions of an individual seeking to maximize received reward in a complex and changing world. Perception-Action Cycle: Adaptive, continuous process of using sensory information to guide a series of goal-directed actions. Behavior Consequences Antecedents

8. http://nrg.mbi.ufl.edu Co-Adaptive BMIs using Reinforcement Learning

9. http://nrg.mbi.ufl.edu Prerequesites for Symbiosis

10. http://nrg.mbi.ufl.edu Co-Adaptive BMI involves TWO intelligent agents involved in a continuous dialogue!!! ROBOT actions rewards brain states RAT’S BRAIN environment RAT’S BRAIN COMPUTER AGENT

11. http://nrg.mbi.ufl.edu Decoding using Reinforcement Learning Rather than knowledge of the kinematic hand trajectory only a performance score is supplied. The score could represent reward or penalty, but does not directly provide information about how to correct for the error. Reward based learning - try to choose strategy to maximize rewards. RL originated from optimal control theory in Markov Decision Processes.

12. http://nrg.mbi.ufl.edu Experimental Co-Adaptive BMI Paradigm Incorrect Target Correct Target Starting Position Match LEDs Grid-space Match LEDs Rat’s Perspective Water Reward Map workspace to grid Rat Robot Arm Left Lever Right Lever 27 discrete actions 26 movements 1 stationary

13. http://nrg.mbi.ufl.edu Agent - Value function estimation

14. http://nrg.mbi.ufl.edu Evidence for Symbiosis Valuation Change in Computer Agent Brain Reorganization Overall Performance

15. http://nrg.mbi.ufl.edu Key Concepts for the Future Fully implantable interfaces are only half of the story. Sharing of goals enables brain-computer dialogue and symbiosis Need for intelligent decoders that assist and co-adapt with the user.

16. http://nrg.mbi.ufl.edu History of Man-Machine Interaction “Implanting tiny machines into the nerves of the heart would make us less human” Today, over half a million pacemakers are implanted annually! We are at the frontier for integrating machines with the nervous system to restore and enhance function. Nicolelis

17. http://nrg.mbi.ufl.edu Tremendous team effort! Jack DiGiovanna - BME Babak Mahmoudi - BME This work is supported by NSF project No. CNS-0540304 Jose Principe - ECE Jose Fortes - ECE

18. http://nrg.mbi.ufl.edu Please visit the lab website for publications and additional information. Neuroprosthetics Research Group http://nrg.mbi.ufl.edu