Movable Electrodes & Feature-Based Decoding S. Cao , Z. Nenadic, D. Meeker, R. Andersen E. Branchaud, J. Cham, J. Burdick Engineering & Applied Science Biology • Get the max yield of high quality signals • Extract max info from (non- optimal?) neurons Electrical Signal Feature Based Spike Decoding Feature Based LFP Decoding Reach State Reach State Prostheti c Control Signal Goals: (hardware ) (software)
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Movable Electrodes & Feature- Based Decoding S. Cao, Z. Nenadic, D. Meeker, R. Andersen E. Branchaud, J. Cham, J. Burdick Engineering & Applied Science.
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Movable Electrodes & Feature-Based Decoding
S. Cao , Z. Nenadic, D. Meeker, R. Andersen E. Branchaud, J. Cham, J. Burdick Engineering & Applied Science Biology
• Get the max yield of high quality signals
• Extract max info from (non-optimal?) neurons
Electrical
Signal
Feature Based Spike Decoding
Feature Based LFP Decoding
Reach
State
Reach
State
Prosthetic Control Signal
Goals:
(hardware)
(software)
Limitations of Neuro-Probes for Chronic Recording
Key Challenge: record high quality signals from many neurons for months/years
Fixed positioning of implant• Non-optimal (or wrong!) receptive fields.
• Non-optimal cell type
• Electrode not near cell body:
Array moves in brain matrix
Inflammation, Gliosis, encapsulation, …
Movable electrodes could: • track movement due to migration
• improve SNR
• overcome implant errors
• find “better” neurons
• break through encapsulation
Make the electrodes movable!(autonomously controlled)
Limitations of Neuro-Probes for Chronic Recording
Current Research Program Outline
Theory – develop probe control algorithms using computational model
• Model extra-cellular neuron potentials
• Control algorithm development guided by computational model
Poisson Spike Trains with repeatable spikes at specific times
Identified Features
Decode Performance
• MFR = 25%
• Feature: 91%
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Projection Coefficient Value
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babi
lity
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Mean Firing Rate Value
Pro
babi
lity
Single neuron decoding comparison(PRR Neuron, left-right reach task)
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alue
Mean Firing Rate Optimal Feature
Coef value
Probability
Decoding Performance 52.5% 68.0%
Feature Probability
Optimal Feature
Multiple Neuron Performance Comparison
8-direction decoding using up to PRR 41 neurons(from single electrode acute recordings)
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4 neurons with no obvious MFR tuning All 41 available neurons
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-red MI
-blue MFR
Shiyan
Talk about the 41 neuron data set first....how they are handpickedMention the 41 neuron is not representative of the neural prosthetic because when you stick a probe in there, you cannot pick the MFR tuned neurons as you like...therefore, the other case is more realistic...and we need to get as much information out of our data as poissible.
Step 1. Estimate the firing rate function from the spike train ensemble
• Wavelet thresholding method [Donoho 1994]
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lemda(t) = 10sin(4pit/512)+15
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Firing rate function estimation using wavelet thresholding
otherwise
ifsign jkjkjkjk
0
)(*'
jk
dbjkjk t)(*
1
0
)(T
l
dbjk
nsljk l
Projecting Noising Estimation
Thresholding
Denoising
Shiyan
Mention there are many ways of denoising, here we choose wavelet thresholding...Also, need to stress simulation.
Step 2: Computing the Theoretical Wavelet Packet Coefficient Distribution
If the spike train process is a homogeneous Poisson …
TN
N
N
eN
T
nN
NnvP
)!2(2/
2
2
1)(
2
0
2
TN
N
N
eN
TnN
NnvP
)!2(2
12
2
1)(
2
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even
odd-10 -8 -6 -4 -2 0 2 4 6 8 100
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0.5Prob of Projection Coef of Homogeneous Poisson of diff rate
10 Hz20 Hz30 Hz40 Hz
Pro
ba
bility
Coefficient Value
If the spike train process is an inhomogeneous Poisson …
Computational method that computes the probabilities exists
Example Distribution of Inhomogeneous Poisson Process
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0.2lemda = 10*step(t)+10*step(t-256), Wavelet Packet coeffcient at level 9
NOTE: The error on the probability P*(v) caused by the estimation error of the rate function decays exponentially with the number of spike trains in the ensemble
Shiyan
Relate the change of firing rate to actual firing rate change with respect to stimulus. Again, stress simulationMention the non-center is an indicator of inhomo Poisson
Step 3. Estimate the empirical distribution of the wavelet packet coefficients
• Each wavelet packet coefficient is integer valued
Step 4: Goodness-of-fit Test between the Theoretical and Empirical Distributions
Use 2 test to assess the difference between the two distributions
Mv
vv jk
jkjkjk vP
vvPvvP
1)(
)]()([*
2*2 DOF is the cardinality of the coefficient vjk
If p-value > 0.95, the coefficient’s distribution deviates significantly from its Poisson counterpart
If p-value < 0.95, both distributions conform
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ResultsResult 1: Cyclic Poisson Process
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Δt
Δt
T
vvaluepvkjkj
j
}95.0)(|{# **
*
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Scale
j
t = 32
t = 64
Δt
Δt
Shiyan
Lead into it with how other methods won't workStress how COV would fail. Also describe the scale information: when deltaT becomes big, the wavelet at a large scale is able to detect the repetition of structure. That's why the coefficiens jumps at a large scale, not at small scales.
Results (II)Result 2: Brandman-Nelson Non-renewal Model [Brandman 2002]
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vvaluepvkjkj
j
}95.0)(|{# **
*
Scale
j
b = 0.5
b = 0.25
As slope b decreases, the scale of renewal increases; equivalently, the process becomes more Poisson like.
Spike Train
Generating Process
Shiyan
Just mention we have a non-renewal process, and our method reveals information that agrees with the original observaion.
Poisson Scale-Gram
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Characterize Poisson-ness at different scales (i.e., is rate coding appropriate?)
Short time-scale non-Poisson-ness
Longer time-scale non-Poisson-ness
Relatively Poisson
Populations of PRR neurons during virtual reach experiments (D. Meeker)
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Tim
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ale
Coefficient index
First Experimental Results(monkey Parietal Reach Region)