COMPUTATIONAL NEUROSCIENCE

Introduction to Computational Neuroscience

1. Lecture (1/13) Motivations from physics and biology

2. Lecture (1/15) Neurons: biological, electronic, simulated

3. Lecture (1/20) Synapses: interaction, learning, Hebb's idea

   Computing with Neural Networks

4. Lecture (1/22) Networks of neural elements

5. Lecture (1/27) Lyapunov functions

6. Lecture (1/29) Associative memory

7. Lecture (2/3) Optimization and constraints

   Single Cell Computation and Neural Codes

8. Lecture (2/5) Single cells

9. Lecture (2/10) Neural codes, rates, spikes, ensembles

   Quiz One (2/10 --- 2/17, no class on 2/12)

10. Lecture (2/17) Linear filters in biology, receptive field

11. Lecture (2/19) Reverse correlation (Mohammad)

12. Lecture (2/24) Signal reconstruction

    Information Processing and Optimization

13. Lecture (2/26) Information theory

14. Lecture (3/2) Factorial coding, decorrelation

15. Lecture (3/4) Information maximization arguments

    Spring break (3/8 --- 3/13)

16. Lecture (3/16) Redundancy reduction

17. Lecture (3/18) Optimal coding

    Learning and Self Organizing Systems

18. Lecture (3/23) Hebbian learning

    Quiz Two (3/23 --- 3/30, no class on 3/25)

19. Lecture (3/30) Time factor and spiking dynamics (Mohammad)

20. Lecture (4/1) Self organization, Kohonen maps

21. Lecture (4/6) Statistical learning theory

22. Lecture (4/8) Development, unsupervised learning in biological modeling

    Additional Topics

23. Lecture (4/13) Stochastic networks

24. Lecture (4/15) Source separation

25. Lecture (4/20) Sparse coding, independent component analysis (Mohammad)

    Final Exam (4/20 --- 4/29)

Send comments to Dawei Dong:
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Dawei W. Dong                                         FAX: (561)297-3634
Center for Complex Systems & Brain Sciences         Phone: (561)297-2326
Florida Atlantic University                       dawei@dove.ccs.fau.edu
777 Glades Road, Boca Raton, FL33431      http://dove.ccs.fau.edu/~dawei
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