1. Chapter 6: Feedforward mapping networks
Fundamentals of
Computational Neuroscience
Chapter 6: Feedforward mapping networks
Dec 09

2. Digital representation of a letter

3. Examples given by lookup table

4. The population node as perceptron

5. How to find the right weight values: learning

6. Example: OCR

7. Example: Boolean functions

8. PerceptronTrain.m

9. The multilayer perceptron (MLP)

10. The error-backpropagation algorithm

11. mlp.m

12. MLP for XOR function

13. MLP approximating sine function

14. Overfitting and underfitting

15. Support Vector Machine (SVM)

16. SVM: Kernel trick

17. Further readings