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

• 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