Computation Sensory Motor-Systems Lab - Prof. Ralph Etienne-Cummings

1. Computation Sensory Motor-Systems Lab- Prof. Ralph Etienne-Cummings Modeling life in silicon

2. The Big Picture: Lab Motivation Developing Biomorphic Robotics Adaptive Biomorphic Circuits & Systems Restoring function after limb amputation Restoring locomotion after severe spinal cord injury

3. Computation Sensory Motor-Systems LabRalph Etienne-Cummings’ Lab • Towards a Spinal Neural Prosthesis Device • Decoding Individual Finger Movements Using Surface EMG Electrodes • Normal Optical Flow Imager • Integrate-and-Fire Array Transceiver • Optimization of Neural Networks • Design of Ultrasonic Imaging Arrays for Detection ofMacular Degeneration • Precision Control Microsystems

4. Towards a Spinal Neural Prosthesis Device Jacob Vogelstein Francesco Tenore

5. Our Approach • RS • Muscles • SLP • Source: Grillner, Nat Rev Neurosci, 2003 Previous approaches ignore CPG and focus on controlling muscles to generate locomotion We propose to directly control the CPG and use it to generate locomotion Basic idea is to recreate natural neural control loop in an external artificial device (i.e. replace tonic and phasic descending inputs to the CPG with electrical stimulation)

6. The Big Picture: Lab Motivation Developing Biomorphic Robotics • Adaptive • Biomorphic Circuits & • Systems Restoring locomotion after severe spinal cord injury Restoring function after limb amputation

7. Responsibilities of Locomotion Controller 1. Select Gait+ specify desired motor output - phase relationships - joint angles 2. Activate CPG + tonic stimulation initiates locomotion - epidural spinal cord stimulation (ESCS) - intraspinal microstimulation (ISMS) 4. Control Output of CPG + phasic stimulation (efferent copy required for precisely-timed stimuli) - convert baseline CPG activityinto functional motor output - correct deviations - adjust individual components - adapt output to environment Select gait ~ brain Activate CPG ~ brainstem (MLR) Efferent copy ~ efferent copy Enforce/adapt output ~ phasic RS 3. Generate “Efferent Copy” + monitor sensorimotor state - external sensors on limbs - internal afferent recordings

8. Gait Control System Analog signal processing front-end Spike processing back-end Source: Vogelstein et al., IEEE TBioCAS, (submitted) 12 pairs of IM electrodes: 3 each for left/right hip, knee, and ankle extensors/flexors Two types of sensory data were collected for each leg Hip angle (HA) Ground reaction force (GRF)

9. Results: SiCPG Chip Controls Locomotion in a Paralyzed Cat Source: Vogelstein et al., IEEE TBioCAS (submitted)

10. Decoding Individual Finger Movements Using Surface EMG Electrodes Francesco Tenore

11. Problem Fast pace of development of upper-limb prostheses requires a paradigm shift in EMG-based controls Traditional control schemes typically provide 2 degrees of freedom (DoF): Insufficient for dexterous control of individual fingers Surface ElectroMyoGraphy (s-EMG) electrodes placed on the forearm and upper arm of an able bodied subject and a transradial amputee

12. Implemented Solution Neural network based approach Number of electrodes (inputs)  amputation level (I-V)  Level I: 32 electrodes, Level V: 12 electrodes

13. Results High decoding accuracy: Trained able-bodied subject, ~99% Untrained transradial amputee, ~ 90% No s.s. difference in decoding accuracy between able-bodied subjects and transradial amputee No s.s. difference in decoding accuracy between networks that used different number of electrodes (12-32)

14. Current/Future Work Towards real-time control: training on rest states and movements  Implementation on Virtual Integration Environment (VIE) Independent Component Analysis (ICA) to minimize number of electrodes by choosing the ones that most contribute to the accuracy results

15. Normal Optical Flow Imager Andre Harrison

16. Normal Optical Flow Imager Computer Vision Neuromorphic ADC

17. Normal Optical Flow Imager • Imager that computes 2-D dense Normal Optical Flow estimates using spatio-temporal image gradients, without interfering with the imaging process • Optical Flow is the apparent motion of the image intensity

18. Normal Optical Flow Imager

19. Integrate-and-Fire Array Transceiver Fopefolu Folowosele

20. Motivation The brain is capable of processing sensory information in real time, to analyze its surroundings and prescribe appropriate action Software models run slower than real time and are unable to interact with the environment Silicon designs take a few months to be fabricated, after which they are constrained by limited flexibility

21. IFAT The IFAT combines the speed of dedicated hardware with the programmability of software for studying real-time operations of cortical, large-scale neural networks

22. Application: Visual Processing

23. Optimization of Neural Networks Alex Russel and Garrick Orchard

24. Pre Evolution Architecture

25. Evolved Hip Controller

26. Evolved Knee Controller

27. The Final Product

28. Design of Ultrasonic Imaging Arrays the Detection ofMacular Degeneration Clyde Clarke

29. Design of Ultrasonic Imaging Arrays the Detection of Macular Degeneration www.seewithlasik.com/.../CO0077.jpg

30. L L W W Tool-tip Mounted Ultrasonic Micro-Array • Create Models of Transducer array operating in Homogeneous Media B. Derive Equations for Wave Propagation in Vitreous and Retina • Scattering • Absorption [Yakub,IEEE Trans 02] C. Numerical Modeling • Finite Element Method • Finite Difference Method • Modify Design Parameters of Array to perform optimally in Surgical Environment

31. Adaptive and Reconfigurable Microsystems for High Precision Control Ndubuisi Ekewe

32. Adaptive and Reconfigurable Microsystems for High Precision Control Simaan, 2004 Ekekwe et al, US Patent (Pending)