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Introduction to Neural Networks >> Content Detail



Lecture Notes



Lecture Notes

Lec #Topics
1From Spikes to Rates (PDF)
2Perceptrons: Simple and Multilayer
3Perceptrons as Models of Vision
4Linear Networks
5Retina
6Lateral Inhibition and Feature Selectivity (PDF 1) (PDF 2) (PDF 3)
7Objectives and Optimization
8Hybrid Analog-Digital Computation

Ring Network
9Constraint Satisfaction

Stereopsis
10Bidirectional Perception
11Signal Reconstruction
12Hamiltonian Dynamics (PDF)
13Antisymmetric Networks (PDF)
14Excitatory-Inhibitory Networks (PDF)

Learning
15Associative Memory
16Models of Delay Activity

Integrators
17Multistability

Clustering
18VQ (PDF)

PCA (PDF)
19More PCA

Delta Rule (PDF)
20Conditioning (PDF)

Backpropagation (PDF)
21More Backpropagation (PDF)
22Stochastic Gradient Descent
23Reinforcement Learning
24More Reinforcement Learning
25Final Review

 








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