1. The Visual System: Feature Detection Model
Lecture 15

2. Cortical Mechanisms Primary Visual Cortex - V1
Striate or calcarine cortex
Hubel and Wiesel - Nobel Prize
Single cell recording
Feature detection model
simple stimuli  complex perceptions ~

3. Feature Detection Model
Visual perception of objects
detection of simple features
points, lines, corners, curves
Convergence of information
retina  LGN V1
Primary Visual Cortex (V1)
Simple cells
complex cells
hypercomplex cells ~

4. Simple Cells Detect lines & edges not center-surround Best stimulus
bar of light or line
particular orientation
particular location ~

5. Simple Cells LGN neurons RF center-surround
converge onto simple cells (V1)
Bar of light
 APs in simple cell ~

6. Simple Cells LGN V1 wrong location or angle
doesn’t affect simple cell ~

7. Complex Cells In V1 & V2 (secondary visual cortex) Convergence
of LGN & simple cells
responds to movement
Best stimulus
line or bar
Fixed angular orientation
Location not as important
preferred direction of movement ~

8. Complex Cells S1 S2 S3 V1
V1/V2
Complex
Cell

9. Hypercomplex Cells V1 & V2 Complex cells converge Hypercomplex cells
Best stimulus
lines of specific length
Detect Corners ~

10. Feature Detection Theory
Features = lines & corners
Can it represent a curve?