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Cortical Neural Prosthetics
Presented by Artie Wu
Cortical Neural Prosthetics
• Andrew B. Schwartz
– Department of Neurobiology and Bioengineering, University of
Pittsburgh
– Annual Review of Neuroscience. 2004. 27:487-507
Outline
• Background on Neural Prosthetics
• Motivation
• Electrodes
– Microwires
– ‘Michigan’ probes
– Cyberkinetics probes
• Complications
• Extraction Algorithms
• FLAMES: Floating Light Activated Micro
Electrical Stimulator
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
Current Benefits of Neural Prostheses
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Restore hearing, vision
Alleviate symptoms of Parkinson’s Disease (PD)
Rid Tourette syndrome
Mitigate head or spinal-cord trauma
Restore movement in paralyzed patients
Ridding Tourette
Source: University Hospitals of Cleveland, affiliated with CWRU
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
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
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
Electrodes: Microwires
• First chronic recording electrodes
• 20-50μm diameter
• Optimal insertion depth uncertain
Electrodes: Silicon Micromachined
Microprobes
• ‘Michigan’ probe
– Planar devices
– Boron diffusion delineates shape of probe
– Multiple recording sites along shaft
Electrodes: Silicon Micromachined
Microprobes
• Cyberkinetics Inc/University of Utah array
– 100 microelectrodes array on 4mm by 4mm base
– Array inserted into cortex
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
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.
Extraction algorithms: Inferential
• Population Vector Algorithm relates movement
direction and firing rate in motor cortex
D – bo = A ·cosθ = bxmx + bymy + bzmz
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
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
FLAMES: Floating Light Activated
Micro Electrical Stimulators
Source: Steve Menn
FLAMES: Floating Light Activated
Micro Electrical Stimulators
Source: Steve Menn
FLAMES: Floating Light Activated
Micro Electrical Stimulators
Source: Steve Menn
Round 3 Design Specs
• 56 different devices
• 1,2,3,4 diodes
• With and without 50kΩ
parallel resistor
Thank you