1. 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)

2. 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, …

3. 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

4. Current Research Program Outline Theory – develop probe control algorithms using computational model Model extra-cellular neuron potentials Control algorithm development guided by computational model Hardware– meso-scale test-beds Validate concept, evaluate algorithms Determine spec.s for MEMS devices Test biomechanics of movable electrodes Experiments– verify theory

5. Single Cell Extracellular Potential Simulation (adapted from Holt & Koch ’98) 3720 compartment NEURON pyramidal cell model ( adapted from Mainen & Sejnowski ‘96 ) Synaptic inputs scattered uniformly throughout dendrites. Laplace equation: Boundary condition: Since solution nearly impossible, use line source approximation (Holt & Koch ‘99) soma

6. Spatio-temporal variations of extracellular potential

7. Virtual experiment Add neural noise

8. Keep electrode in this region! Quality MetricIsolation curve How to find the maximum point of the average isolation curve when all we have are noisy observations? Peak-to-Peak Amplitude

9. Solution offered by variant of Stochastic optimization. Basis function approach Iterative Algorithm

10. Experimental Setup Microdrive in the brain Filters / Preamps X Computer with: Data Acquisition Electrode Control algorithm Move Command

11. Experimental Results (monkey Parietal Reach Region) Electrode Position Peak-to-Peak Amplitude Averaged Waveform Cell Isolation Curve Electrode Path

12. Algorithmic State Machine Initial State Spikes Detected? Move Fixed TF Spikes Detected No Spikes Detected

13. Algorithmic State Machine Initial State Spikes Detected? Move Fixed TF Spikes Detected No Spikes Detected

14. Algorithmic State Machine No Spikes Detected Spikes Detected? Move Fixed TF Spikes Detected

15. Algorithmic State Machine No Spikes Detected Spikes Detected? Move Fixed TF Spikes Detected

16. Algorithmic State Machine No Spikes Detected Spikes Detected? Move Fixed TF Spikes Detected

17. Algorithmic State Machine No Spikes Detected Spikes Detected? Move Fixed TF Spikes Detected

18. Algorithmic State Machine Spikes Detected Isolation Curve to maximize? Move Fixed Move Gradien t TF Maximize Isolation Curve

19. Algorithmic State Machine Spikes Detected Isolation Curve to maximize? Move Fixed Move Gradien t TF Maximize Isolation Curve

20. Algorithmic State Machine Spikes Detected Isolation Curve to maximize? Move Fixed Move Gradien t TF Maximize Isolation Curve

21. Algorithmic State Machine Spikes Detected Isolation Curve to maximize? Move Fixed Move Gradien t TF Maximize Isolation Curve

22. Algorithmic State Machine Maximize Isolation Curve Is Cell Isolated? Move Gradien t Do Not Move TF Maintain Isolation

23. Algorithmic State Machine Maximize Isolation Curve Is Cell Isolated? Move Gradien t Do Not Move TF Maintain Isolation

24. Algorithmic State Machine Maintain Isolation Is Cell Isolated? Move small Fixed Do Not Move TF Regain Isolation

25. Algorithmic State Machine Regain Isolation Is Cell Isolated? Move small fixed Do Not Move TF Maintain Isolation Re-isolate when signal falls below threshold

26. Movable Multi-Electrode Testbed sub-micron steps, 1cm range fits in standard chamber many adjustments can insert micro-capillary Test Multi-electrode issues Test electrode/fluid combos gather data for MEMS spec.s “Nanomotors” Chamber Acrylic Skull Dura Brain Tissue

27. Surgical Implantation Special tooling installs and positions the electrode and fluid delivery modules 5mm Motorized tooling fixture brain dura skull Implant and movable probes Stacking chamber and buss connector Implant MEMS movable probes and fluid delivery chips Fluid delivery chip: stores & delivers anti-inflammatory cytokine and neurotrophic factors Movable electrode chip: Low-heat and low-energy hydrolysis bellow actuators move electrodes Fluid injectors Peristaltic electro- lysis pumps Fluid reservoir Movable probe Low-heat hydrolysis actuators Control electronics

28. MEMS Electrolysis Actuator Concept (with Y.C. Tai)  Large Force Generation  Low Temperature  Low Power  Lockable Electrode Bellows Z-Movement Actuator Electrolysis Electrodes

29. Feature Based Bayesian Decoding Characterize receptive Fields Predict movement plan x=argmax[P(x|v)] In real time, record cell activities What features to use?

30. Decoding the Planned Reach Direction Bayesian Classifier Firing RateFeature Extraction x-Reach Direction v-Feature

31. Wavelet Packet Overview Wavelet Packet TreeHaar Wavelet Packet up to Level 14 Number of spiking in a window (firing rate) Local change of firing rate (slope in PSTH) Local oscillation in spiking train (bursting) LH LH

32. Feature Selection Goal: select the most informative wavelet bases (features) X is reach class p(v|X) is conditional probability of feature v given class X Solution: choose cost function to quantify the decodability of each feature ) Mutual Information Spike train Basis Functions

33. Wavelet Packet Tree Pruning Prune the wavelet packet tree in searching for the most informative features. Features with large mutual information Features that are orthogonal to each other Feature Template t

34. Simple Sanity Check Poisson Spike Trains with repeatable spikes at specific times Identified Features Decode Performance MFR = 25% Feature: 91%

35. Single neuron decoding comparison (PRR Neuron, left-right reach task) Mean Firing RateOptimal Feature Coef value Probability Decoding Performance 52.5% 68.0% Feature Probability Optimal Feature

36. Multiple Neuron Performance Comparison 8-direction decoding using up to PRR 41 neurons (from single electrode acute recordings) 4 neurons with no obvious MFR tuningAll 41 available neurons -red MI -blue MFR

37. Step 1. Estimate the firing rate function from the spike train ensemble Wavelet thresholding method [Donoho 1994] Firing rate function estimation using wavelet thresholding Projecting Noising Estimation Thresholding Denoising

38. Step 2: Computing the Theoretical Wavelet Packet Coefficient Distribution If the spike train process is a homogeneous Poisson … even odd Probability Coefficient Value If the spike train process is an inhomogeneous Poisson … Computational method that computes the probabilities exists

39. Example Distribution of Inhomogeneous Poisson Process (t)=10step(t)+10step(t-256) time frequency 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

40. Step 3. Estimate the empirical distribution of the wavelet packet coefficients Each wavelet packet coefficient is integer valued Histogram rule estimation Coefficient Value Probability

41. Step 4: Goodness-of-fit Test between the Theoretical and Empirical Distributions Use  2 test to assess the difference between the two distributions DOF is the cardinality of the coefficient v jk If p-value > 0.95, the coefficient’s distribution deviates significantly from its Poisson counterpart If p-value < 0.95, both distributions conform

42. Results Result 1: Cyclic Poisson Process Δt Scale jj  t = 32  t = 64 Δt

43. Results (II) Result 2: Brandman-Nelson Non-renewal Model [Brandman 2002] Scale j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

44. Poisson Scale-Gram 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) Time scale Coefficient index

45. First Experimental Results (monkey Parietal Reach Region) Cell Isolation Curve Electrode Path