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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

- 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

- 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

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

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

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

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

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

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

Complex Horizontal and Vertical Line Detector Matrix

- Experiment 1
- 6 Line-Segment Letters without Line Detectors
- 26 Letters without Line Detectors

- Experiment 2
- 6 Line-Segment Letters with Line Detectors

- 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%

Experiment 1

- 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

6 Letters: no line detectors

50 Hidden layer units

50 Epochs

0% noise

6 Letters: no line detectors

50 Hidden layer units

1600 Epochs

0% noise

6 Letters: with line detectors

50 Hidden layer units

50 Epochs

0% noise

6 Letters: with line detectors

50 Hidden layer units

50 Epochs

0% noise

6 Letters: no line detectors

10 Hidden layer units

1600 Epochs

0% noise

6 Letters: with line detectors

10 Hidden layer units

1600 Epochs

0% noise

6 Letters: with line detectors

10 Hidden layer units

1600 Epochs

0% noise

26 Letters: no line detectors

50 Hidden layer units

1600 Epochs

0% noise

26 Letters: no line detectors

50 Hidden layer units

1600 Epochs

0% noise

- 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

- 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