Human visual system neural network
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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. The Visual System. Hubel and Wiesel 1981 Nobel Prize for work in early 1960s

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Human Visual System Neural Network

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Human visual system neural network

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


The visual system

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


The study

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


Uppercase 5x7 bit map alphabet horizontal vertical line segment letters are e f h i l t

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


Neural network without line detectors

Neural Network Without Line Detectors


Neural network specification without line detectors

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


Neural network with line detectors

Neural Network With Line Detectors


Neural network specification with vertical and horizontal line detectors

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


Vertical line detectors

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


Retina image letter e in upper left area

Retina Image – Letter “E” in Upper Left Area

Region of possible upper-left corners is shown in green.


Retina image letter e in upper right area

Retina Image – Letter “E” in Upper Right Area

Region of possible upper-left corners is shown in green.


Retina image letter e in lower right area

Retina Image – Letter “E” in Lower Right Area

Region of possible upper-left corners is shown in green.


Example of vertical line detector on line segment of e detector activated

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


Example of shifted vertical line detector on letter e detector not activated

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


Example of shifted vertical line detector on letter e detector not activated1

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


Human visual system neural network

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


Human visual system neural network

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


The corresponding 48 complex horizontal and vertical line detectors

The Corresponding 48 Complex Horizontal and Vertical Line Detectors

Complex Horizontal and Vertical Line Detector Matrix


Experiments

Experiments

  • Experiment 1

    • 6 Line-Segment Letters without Line Detectors

    • 26 Letters without Line Detectors

  • Experiment 2

    • 6 Line-Segment Letters with Line Detectors


Experimental parameter combinations

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%


Simulation view peltarion s synapse product

Simulation View – Peltarion’s Synapse Product

Experiment 1


Simulation settings experiment 2 line detectors 6 line segment letters e f h i l t

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


Exp 1 6 letters no line detectors 35 42 accuracy

Exp 1 – 6 Letters, No Line Detectors – 35.42% Accuracy

6 Letters: no line detectors

50 Hidden layer units

50 Epochs

0% noise


Exp 1 6 letters no line detectors 36 25 accuracy

Exp 1 – 6 Letters, No Line Detectors – 36.25% Accuracy

6 Letters: no line detectors

50 Hidden layer units

1600 Epochs

0% noise


Exp 2 6 letters with line detectors 67 5 accuracy

Exp 2 – 6 Letters, With Line Detectors – 67.5% Accuracy

6 Letters: with line detectors

50 Hidden layer units

50 Epochs

0% noise


Exp 2 6 letters with line detectors 67 5 accuracy1

Exp 2 – 6 Letters, With Line Detectors – 67.5% Accuracy

6 Letters: with line detectors

50 Hidden layer units

50 Epochs

0% noise


Exp 1 6 letters no line detectors 27 69 accuracy

Exp 1 – 6 Letters, No Line Detectors – 27.69% Accuracy

6 Letters: no line detectors

10 Hidden layer units

1600 Epochs

0% noise


Exp 1 6 letters with line detectors 82 5 accuracy

Exp 1 – 6 Letters, With Line Detectors – 82.5% Accuracy

6 Letters: with line detectors

10 Hidden layer units

1600 Epochs

0% noise


Exp 2 6 letters with line detectors 82 5 accuracy

Exp 2 – 6 Letters, With Line Detectors – 82.5% Accuracy

6 Letters: with line detectors

10 Hidden layer units

1600 Epochs

0% noise


Exp 1 26 letters no line detectors 27 69 accuracy

Exp 1 – 26 Letters, No Line Detectors – 27.69% Accuracy

26 Letters: no line detectors

50 Hidden layer units

1600 Epochs

0% noise


Exp 1 26 letters no line detectors 27 69 accuracy1

Exp 1 – 26 Letters, No Line Detectors – 27.69% Accuracy

26 Letters: no line detectors

50 Hidden layer units

1600 Epochs

0% noise


Exp 1 6 letters no line detectors epochs versus percent added noise

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


Exp 1 26 letters no line detectors epochs versus percent added noise

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


Exp 2 6 letters with line detectors epochs versus percent added noise

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


Comparison of line no line detector networks 6 letters 50 hidden layer units 1600 epochs no noise

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


Main conclusion

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


Additional conclusions

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


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