The Visual System: Feature Detection Model

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

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. S1 S2 S3 Complex Cells 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?