1. Human Visual System Neural Network Stanley Alphonso, Imran Afzal, Anand Phadake, Putta Reddy Shankar, and Charles Tappert

2. Agenda Introduction – make a case for the study –The Visual System –Biological Simulations of the Visual System –Machine Learning and Artificial Neural Networks (ANNs) –ANNs Using Line and/or Edge Detectors –Current Study Methodology Experimental Results Conclusions Future Work

3. Introduction - The Visual System The Visual System Pathway –Eye, optic nerve, lateral geniculate nucleus, visual cortex 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

4. Introduction - Computational Neuroscience Biological Simulations of the Visual System Hubel-Wiesel discoveries instrumental in the creation of what is now called computational neuroscience Which studies brain function in terms of information processing properties of structures that make up the nervous system Creates biologically detailed models of the brain 18 November 2009 – IBM announced they created the largest brain simulation to date on the Blue Gene supercomputer – millions of neurons and billions of synapses exceeding those in the cat’s brain

5. Introduction – Artificial Neural Networks (ANNs) Machine learning scientists have taken a different approach using simpler neural network models called ANNs Commonest type used in pattern recognition is a feedforward ANN Typically consists of 3 layers of neurons –Input layer –Hidden layer –Output layer

6. Introduction – Simple Feedforward Artificial Neural Network (ANN)

7. Introduction - Literature review of ANNs using line/edge detectors GIS images/maps – line and edge detectors in four orientations – 0°, 45°, 90°, and 135° Synthetic Aperture Radar (SAR) images – line detectors constructed from edge detectors Line detection can be done using edge techniques such as Sobel, Prewitt, Laplacian Gaussian, Zero Crossing and Canny edge detector

8. Introduction - Current Study Use ANNs to simulate line and edge detectors known to exist in the human visual cortex Construct two feedforward ANNs – one with line detectors and one without – and compare their accuracy and efficiency on a character recognition task Demonstrate superior performance using pre- wired line and edge detectors

9. Methodology Character recognition task - classify straight line uppercase alphabetic characters Experiment 1 – ANN without line detectors Experiment 2 – ANN with line detectors Compare –Recognition accuracy –Efficiency – training time

10. Alphabetic Input Patterns Six Straight Line Characters (5 x 7 bit patterns) ***** ***** * * * * ***** * * * * * * * **** **** ***** * * * * * * * * * * ***** * * * * ***** *

11. Experiment 1 - ANN without line detectors

12. Alphabet character can be placed in any position inside the 20x20 retina not adjacent to an edge – 168 (12*14) possible positions Training – choose 40 random non-identical positions for each of the 6 characters (~25% of patterns) –Total of 240 (40 x 6) input patterns –Cycle through the sequence E, F, H, I, L, T forty times for one pass (epoch) of the 240 patterns Testing – choose another 40 random non-identical positions for each character for total 240

13. Input patterns on the retina E(2,2) and E(12,5) 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

14. Experiment 2 - ANN with line detectors

15. Simple horizontal and vertical line detectors Horizontal Vertical + --- -+- +++++ -+- --- -+- + 288 horizontal and 288 vertical line detectors for a total of 576 simple line detectors

16. 24 complex vertical line detectors and their feeding 12 simple line detectors

17. Results – No Line Detectors 10 hidden-layer units EpochsTraining Time Training Accuracy Testing Accuracy 50~2.5 hr100%26.7% 100~4 hr100%28.3% 200~8 hr100%28.8% 400~16 hr100%30.4% 800~30 hr100%28.3% 1600~2 days100%23.8% Average100%27.7%

18. Results – Line Detectors 10 hidden-layer units EpochsTraining Time Training Accuracy Testing Accuracy 500:37 min47.5%37.5% 1000:26 min100.0%63.3% 2000:51 min100.0%68.8% 4002:28 min71.3%50.8% 8003:37 min100.0%67.9% 16008:42 min95.8%56.7% Average85.8%57.5%

19. Line Detector Results 50 hidden-layer units Epochs Set/ Attained Training Time Training Accuracy Testing Accuracy 50/841 sec100%70.0% 100/945 sec100%69.8% 200/1048 sec100%71.9% 400/1049 sec100%77.1% 800/841 sec100%72.5% 1600/945 sec100%71.3% Average100%72.1%

20. Confusion Matrix Overall Accuracy of 77.1% Out In EFHILT E62.52000512.5 F 80002.55 H07.58507.50 I0509500 L0152.5572.55 T2.520010067.5

21. Conclusion - Efficiency ANN with line detectors resulted in a significantly more efficient network –training time decreased by several orders of magnitude

22. Conclusion - Recognition Accuracy

23. Conclusion – Efficiency Compare Fixed/Variable Weights ExperimentFixed Weights Variable Weights Total Weights 1 No Line Detectors 020,300 2 Line Detectors 6,9122,7009,612

24. Conclusion The strength of the study was its simplicity The weakness was also it simplicity and that the line detectors appear to be designed specifically for the patterns to be classified Weakness can be corrected in future work

25. Future Work Other alphabetic input patterns * **** *** * * * * * * * * * * * * * **** * ***** * * * * * * * * * * * **** ***

26. Simple horizontal and vertical edge detectors --- +++ +++ --- -+ +-

27. Questions