1. Neural Decoding: Classifiers in action Malena I. Español SIAM Student Chapter at Tufts November 19 th, 2007

2. SIAM Student Chapter at Tufts Introduction Kreiman Lab at Children’s Hospital Boston Main lines of research: 1) How circuits of neurons represent visual information 2) How gene expressions is orchestrated, with a particular emphasis on gene expression in the nervous system The Lab uses a combination of mathematical, computational and experimental tools.

3. SIAM Student Chapter at Tufts Experiment

4. SIAM Student Chapter at Tufts Experiment We have many electrodes (channels)

5. SIAM Student Chapter at Tufts Experiment We show several pictures

6. SIAM Student Chapter at Tufts Categorization: Object

7. SIAM Student Chapter at Tufts Linear Learning Machine

8. SIAM Student Chapter at Tufts Linear Learning Machine

9. SIAM Student Chapter at Tufts Ill-posed Problem A problem is ill-posed if 1) its solution does not exist, or 2) its solution is not unique, or 3) its solution does not depend continuously on the data

10. SIAM Student Chapter at Tufts Linear Learning Machine

11. SIAM Student Chapter at Tufts Ill-posed problem

12. SIAM Student Chapter at Tufts Response Options: N Areas N Highs (sign(x).max(|x|)) Spectral Power (FFT+sum) (mean,std, median) (mean,max, maxposition) (mean, max, median) (min, max, max-min) (minposition, maxposition, minpos-maxpos) (max, maxposition, FFT+sum(Gamma))

13. SIAM Student Chapter at Tufts Process Split points in categories Count how many there are in each Compute the smallest number s Take s points of each category Choose s/2 for training and s/2 for testing Construct classifier using training points Test performance using testing points Bootstrapping: shuffle categories

14. SIAM Student Chapter at Tufts One Vs All (OVA) Compute a binary classifier (one class vs. the rest of the classes) for each class. Take a test point and apply each binary classifier. Choose the class corresponding to the highest outcome.

15. SIAM Student Chapter at Tufts Categorization We have 7 categories: 1.Animals – 2.Chairs – 3.Faces – 4.Fruits – 5.Legos – 6.Shoes – 7.Vehicles 5 categories: 1.Animals – 2.Chairs – 3.Faces – 4.Fruits – 5. old(7). Vehicles

16. SIAM Student Chapter at Tufts Spectral Analysis time ∑4-7.5 Hz ∑ 8-13.5 Hz ∑ 14-29.5 Hz ∑ 30-58 Hz ∑ 62-100 Hz log FFT 2

17. SIAM Student Chapter at Tufts Subject 10 Channels: 42, 73, 76 73-77 73-76, 88 41, 73, 75, 88 41, 73, 81, 87 All Channels Performance 0.582+- 0.016 Performance per Category 1.58 % 2.52 % 3.76 % 4.56 % 5.50 % Selected Channels Performance 0.6463+- 0.015 Performance per Category 1.65 % 2.60 % 3.79 % 4.63 % 5.56 %

18. SIAM Student Chapter at Tufts Subject 6 33, 41, 42, 43, 50 33 42, 49, 57 34, 42, 50 33, 42, 49 42 42, 50

19. SIAM Student Chapter at Tufts Subject 6 All Channels Performance 0.4178+- 0.008 Performance per Category 1.47 % 2.38 % 3.49 % 4.39 % 5.41 % 6.44 % 7.34 % Selected Channels Performance 0.4973+- 0.015 Performance per Category 1.54 % 2.45 % 3.61 % 4.48 % 5.47 % 6.50 % 7.43 %

20. SIAM Student Chapter at Tufts Subject 7 Channels: 13, 101 100 75, 79, 80, 100, 101 12, 97, 99 75, 98 All Channels Performance 0.3603+- 0.0252 Performance per Category 1.40 % 2.34 % 3.43 % 4.34 % 5.28 % Selected Channels Performance 0.4530+- 0.029 Performance per Category 1.48 % 2.37 % 3.61 % 4.40 % 5.41 %

21. SIAM Student Chapter at Tufts References Neural coding: computational and biophysical perspectives by GK A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex by Serre, Kough, Cadieu, Knoblich, Kreiman and Poggio An Introduction to Support Vector Machines by Cristianini, Shawe-Taylor