Introduction to Neural Networks

Lecture Notes

Lec #	Topics
1	From Spikes to Rates (PDF)
2	Perceptrons: Simple and Multilayer
3	Perceptrons as Models of Vision
4	Linear Networks
5	Retina
6	Lateral Inhibition and Feature Selectivity (PDF 1) (PDF 2) (PDF 3)
7	Objectives and Optimization
8	Hybrid Analog-Digital Computation Ring Network
9	Constraint Satisfaction Stereopsis
10	Bidirectional Perception
11	Signal Reconstruction
12	Hamiltonian Dynamics (PDF)
13	Antisymmetric Networks (PDF)
14	Excitatory-Inhibitory Networks (PDF) Learning
15	Associative Memory
16	Models of Delay Activity Integrators
17	Multistability Clustering
18	VQ (PDF) PCA (PDF)
19	More PCA Delta Rule (PDF)
20	Conditioning (PDF) Backpropagation (PDF)
21	More Backpropagation (PDF)
22	Stochastic Gradient Descent
23	Reinforcement Learning
24	More Reinforcement Learning
25	Final Review