AS.050.375, AS.050.675, EN.600.485

Probabilistic Models of the Visual Cortex:
 
Tues/Thurs: 9:00-10:15am Fall 2018, Krieger 111.
 
 

Course Description

The course gives an introduction to computational models of the mammalian visual cortex. It covers topics in low-, mid-, and high-level vision. It briefly discusses the relevant evidence from anatomy, electrophysiology, imaging (e.g., fMRI), and psychophysics. It concentrates on mathematical modelling of these phenomena taking into account recent progress in probabilistic models of computer vision and developments in machine learning.

Reading and Background Material

Grading Plan: 5 homework assignments.

Preliminary Schedule (subject to revision)

Lecture Date Topics

Handouts

Suggested Reading
1

Sept-4

Introduction to Vision
Slides YuilleKersten (Section 1.1, 1.2)
2
Sept-6
The Retina.  Simple Linear Models
Slides
YuilleKersten (Sections 1.3, 2.1)
Bialek   EyeSmarter   Microprocessor   VisualCrowding
3

Sept-11

Brief Introduction to V1. Models of Simple and Complex Cells.
Maths   Kokkinos
YuilleKersten (Sections 1.3,  2.1)
4
Sept-13
Sparsity  and Natural Images
Maths   Sparsity
YuilleKersten (Section 2.2)
Barlow
5

Sept-18

Making Decisions I: Statistical Edge Detection and Segmentation
Slides YuilleKersten (Section 3.1, 3.2)
6
Sept-20 Making Decisions II: Bayes Decision Theory
Slides YuilleKersten (Section 3.1, 3.2)
7

Sept-25

Hebbian Learning and Regression
HebbRegression   Sparsity
YuilleKersten (Section 2.3)
Curtis   Early   Gallant
8

Sept-27

Filters for Binocular Stereo and Motion
Figures   Motion
YuilleKersten (Section 2.4)
9
Oct-2
Context and Spatial Interactions Between Neurons I
ContextMarkov
YuilleKersten (Section 4)
10
Oct-4
Context and Spatial Interactions Between Neurons II ContextMarkov
YuilleKersten (Section 3.3)
11
Oct-9
Context Examples: Weak Membrane, Associative Field
ContextExamples   YuilleXuLei
YuilleKersten (Section 4)
12
Oct-11
Introduction to Deep Networks Slides   LowLevelExamples
YuilleKersten (Section 4)
13
Oct-16
Boltzmann Machines & More Context Examples
Boltzmann   ContextExamples
YuilleKersten (Section 4)
14
Oct-18
Cue Coupling
BasicCueCoupling   CausalCueCombination

15
Oct-23
More Cue Coupling
CausalCueCombination
Primer
16
Oct-25
Deep Networks for Cue Coupling
Slides

17
Oct-30
Perceptrons
Slides
Regression   Perceptron
18
Nov-1
What do Deep Networks do?
Yosinski   ICLR   Stanford

19
Nov-6
Unsupervised Learning
Smirnakis   Holistic
PAMI
20
Nov-8
Attention (Bottom-Up)
Niebur   Saliency
vonderHeydt   EtienneCummings
21
Nov-13
Compositionality (I)
Slides

22
Nov-15
Compositionality (II)
Slides


Nov-20
Thanksgiving



Nov-22
Thanksgiving


23
Nov-27
Vision and Language
Slides

24
Nov-29
High Level Vision
ABS   Parsing

25
Dec-4
Kalman Filtering
Kalman1   Kalman2
Simulation
26
Dec-6
Review of Course
Slides