Human Visual System Neural Network

1. Human Visual System Neural Network Stamatios Cheirdaris, Dmitry Nikelshpur, Charles Tappert, Alexander Cipully, Roberto Rodriguez, Rohit Yalamanchi, Abou Damon, Stephanie Pierce-Jones, and Robert Zucker

2. The Visual System • Hubel and Wiesel • 1981 Nobel Prize for work in early 1960s • Cat’s visual cortex • cats anesthetized, eyes open with controlling muscles paralyzed to fix the stare in a specific direction • thin microelectrodes measure activity in individual cells • cells specifically sensitive to line of light at specific orientation • Key discovery – line and edge detectors in the visual cortex of mammals

3. The Study • Compare Two Neural Networks • One without vertical and horizontal line detectors • One with vertical and horizontal line detectors • Objective • Show that the neural network with line detectors is superior to the one without on the six vertical-horizontal line-segment letters E, F, H, I, L, T • Also, experiment with the full alphabet • Without line detectors

4. Uppercase 5x7 Bit-map AlphabetHorizontal-vertical line-segment letters are E, F, H, I, L, T

5. Neural Network Without Line Detectors

6. Neural Network SpecificationWithout Line Detectors Layers • Input layer: 20x20 retina of binary units • Hidden layer: 50 units (other numbers explored) • Output layer: 6 units for letters E, F, H, I, L, T Weights • 20,000 (400x50) between input and hidden layer • 300 (50x6) between hidden and output layer • Total of 20,300 variable weights, no fixed weights

7. Neural Network With Line Detectors

8. Neural Network SpecificationWith Vertical and Horizontal Line Detectors Layers • Input layer: 20x20 retina of binary units • 576 simple vertical and horizontal line detectors • 48 complex vertical and horizontal line detectors • Hidden layer: 50 units (other numbers explored) • Output layer: 6 units for letters E, F, H, I, L, T Weights • 6336 (576x11) fixed weights from input to simple detectors • 576 fixed weights from simple and complex detectors • 2400 (48x50) variable weights from complex detectors to hidden layer • 300 (50x6) variable weights from hidden to output layer • Total of 6912 fixed weights and 2700 variable weights

9. Vertical Line Detectors DETECTORS OVERLAP COVERING EACH POSSIBLE RETINAL POSITION FOR A TOTAL OF288 (18x16) VERTICAL LINE DETECTORS EACH DETECTOR HAS 5 EXCITATORY AND 6 INHIBITORY INPUTS (11 FIXED WEIGHTS), WITH A THRESHOLD OF 3 Horizontal Line Detectors are Similar

10. Retina Image – Letter “E” in Upper Left Area Region of possible upper-left corners is shown in green.

11. Retina Image – Letter “E” in Upper Right Area Region of possible upper-left corners is shown in green.

12. Retina Image – Letter “E” in Lower Right Area Region of possible upper-left corners is shown in green.

13. Example of Vertical Line Detectoron Line Segment of “E” – Detector Activated

14. Example of Shifted Vertical Line Detectoron Letter “E” – Detector Not Activated

15. Example of Shifted Vertical Line Detectoron Letter “E” – Detector Not Activated

16. 24 Vertical Complex Line Detector RegionsAny Simple Line Detector in a RegionActivates the Complex Line Detector

17. 24 Horizontal Complex Line Detector RegionsAny Simple Line Detector in a RegionActivates the Complex Line Detector

18. The Corresponding 48 Complex Horizontal and Vertical Line Detectors Complex Horizontal and Vertical Line Detector Matrix

19. Experiments • Experiment 1 • 6 Line-Segment Letters without Line Detectors • 26 Letters without Line Detectors • Experiment 2 • 6 Line-Segment Letters with Line Detectors

20. Experimental Parameter Combinations • Epochs: • 50 • 100 • 200 • 400 • 800 • 1600 • 32000 (occasionally) • Hidden Layer Units: • 10 • 18* • 50 • 100 • 200 * • 300* • 500* *Selected cases • Noise: • 0% • 2% • 5% • 10% • 15% • 20%

21. Simulation View – Peltarion’s Synapse Product Experiment 1

22. Simulation Settings Experiment 2 – Line Detectors6 Line-Segment Letters: E, F, H, I, L, T • Function Layer: • Function: Tanh Sigmoid • Forward Rule: No rule • Back Rule: Levenberg-Marquardt • Propagator: Function Layer • Weight Layer: • Forward Rule: No rule • Back Rule: Levenberg-Marquardt • Propagator: Weight Layer

23. Exp 1 – 6 Letters, No Line Detectors – 35.42% Accuracy 6 Letters: no line detectors 50 Hidden layer units 50 Epochs 0% noise

24. Exp 1 – 6 Letters, No Line Detectors – 36.25% Accuracy 6 Letters: no line detectors 50 Hidden layer units 1600 Epochs 0% noise

25. Exp 2 – 6 Letters, With Line Detectors – 67.5% Accuracy 6 Letters: with line detectors 50 Hidden layer units 50 Epochs 0% noise

26. Exp 2 – 6 Letters, With Line Detectors – 67.5% Accuracy 6 Letters: with line detectors 50 Hidden layer units 50 Epochs 0% noise

27. Exp 1 – 6 Letters, No Line Detectors – 27.69% Accuracy 6 Letters: no line detectors 10 Hidden layer units 1600 Epochs 0% noise

28. Exp 1 – 6 Letters, With Line Detectors – 82.5% Accuracy 6 Letters: with line detectors 10 Hidden layer units 1600 Epochs 0% noise

29. Exp 2 – 6 Letters, With Line Detectors – 82.5% Accuracy 6 Letters: with line detectors 10 Hidden layer units 1600 Epochs 0% noise

30. Exp 1 – 26 Letters, No Line Detectors – 27.69% Accuracy 26 Letters: no line detectors 50 Hidden layer units 1600 Epochs 0% noise

31. Exp 1 – 26 Letters, No Line Detectors – 27.69% Accuracy 26 Letters: no line detectors 50 Hidden layer units 1600 Epochs 0% noise

32. Exp 1 – 6 Letters, No Line DetectorsEpochs versus Percent Added Noise

33. Exp 1 – 26 Letters, No Line DetectorsEpochs versus Percent Added Noise

34. Exp 2 – 6 Letters, With Line DetectorsEpochs versus Percent Added Noise

35. Comparison of Line / No-Line Detector Networks6 letters, 50 hidden layer units, 1600 epochs, no noise

36. Main Conclusion • Character recognition performance and efficiency of the neural network using Hubel-Wiesel-like line detectors in the early layers is superior to that of a network using adjustable weights directly from the retina • Recognition performance more than doubled • Line detector network was much more efficient • order of magnitude fewer variable weights and half as many total weights • training time decrease of several orders of magnitude

37. Additional Conclusions • Increasing the number of hidden layer units does not translate to better accuracy, it actually reduces it. • Increasing the number of epochs increased the accuracy but not always • For Experiment 2 (6 letters with line detectors) we can achieve perfect training accuracy and very good validation accuracy • Training time varied from a few minutes to many hours with Experiment 1 – 26 Letters taking the longest out of all, i.e. for 500 hidden layer units it required up to 9 hours. • When noise is added to the retina image the accuracy of the system drops significantly, even for Experiment 2 with the line detectors