1. Cortical Neural Prosthetics Presented by Artie Wu

2. Cortical Neural Prosthetics Andrew B. Schwartz –Department of Neurobiology and Bioengineering, University of Pittsburgh –Annual Review of Neuroscience. 2004. 27:487-507

3. Outline Background on Neural Prosthetics Motivation Electrodes –Microwires –‘Michigan’ probes –Cyberkinetics probes Complications Extraction Algorithms FLAMES: Floating Light Activated Micro Electrical Stimulator

4. Neural Prostheses Stimulating and recording electrodes implanted in cerebral cortex to activate neurons in different parts of CNS Cortical Neural Prostheses (CNP) to control arm movement –Use neural activity to control devices to replace natural, animate movements in paralyzed individuals

5. Current Benefits of Neural Prostheses Restore hearing, vision Alleviate symptoms of Parkinson’s Disease (PD) Rid Tourette syndrome Mitigate head or spinal-cord trauma Restore movement in paralyzed patients

6. Ridding Tourette Source: University Hospitals of Cleveland, affiliated with CWRU

7. Problems of Muscle Activation Muscle activation muscle force is nonlinear problem Primary motor cortex drives motor activation –Depends on force, muscle length, limb geometry, orientation of limb relative to external forces, and inertia of moving segments

8. Representing Movement Current CNPs represent movement as end point displacements –Motor cortical discharge rate proportional to tuning function (discharge rate related to direction) All cells actively code each direction Weighted response gives specific direction using Population Vector Algorithm (PVA) Magnitude and direction of this neural vector representation is highly correlated with movement velocity

9. CNP: 3 Components Record neural activity –Microelectrodes and recording electrodes Extraction algorithm of neural code –Real time data acquisition and conversion to end point positions Actuators –Animated computer displays, movement of robot arm, or activation of muscle

10. Electrodes: Microwires First chronic recording electrodes 20-50μm diameter Optimal insertion depth uncertain

11. Electrodes: Silicon Micromachined Microprobes ‘Michigan’ probe –Planar devices –Boron diffusion delineates shape of probe –Multiple recording sites along shaft

12. Electrodes: Silicon Micromachined Microprobes Cyberkinetics Inc/University of Utah array –100 microelectrodes array on 4mm by 4mm base –Array inserted into cortex

13. Tissue Reactions Blood vessels disrupted and microhemorrhage Astrocytes proliferate and form encapsulation around electrode Cellular sheath around electrode with dense cells Swelling pushes neurons away Neuron density is increases after several weeks

14. Impedance Caused by Encapsulation Source: ‘Chronic neural recordings using silicon microelectrode arrays electrochemically deposited with a poly(3,4-ethylenedioxythiophene) (PEDOT) film’, K. Ludwig, J. Neural Eng. 3. 2006, 59-70.

15. Extraction algorithms: Inferential Population Vector Algorithm relates movement direction and firing rate in motor cortex D – b o = A ·cosθ = b x m x + b y m y + b z m z D: discharge rate A: amplitude of tuning function Θ: angle between cell’s preferred direction B: vector in direction of preferred direction, magnitude is A M: unit vector in movement direction

16. Extraction algorithms: Classifiers Based on pattern recognition Ex: Self-organizing feature map (SOFM) –Single layer of nodes connected to input vector with set of connection weight (i.e., discharge rate) –Initially, weight vectors set randomly –Element with weight vector closest to input vector = winner –Neighbor’s weights moved closer to input vectors –Each cluster assigned direction

17. FLAMES: Floating Light Activated Micro Electrical Stimulators Source: Steve Menn

18. FLAMES: Floating Light Activated Micro Electrical Stimulators Source: Steve Menn

19. FLAMES: Floating Light Activated Micro Electrical Stimulators Source: Steve Menn

20. Round 3 Design Specs 56 different devices 1,2,3,4 diodes With and without 50kΩ parallel resistor

21. Thank you