An introduction to computational neuroscience, i.e. the application of of computational and mathematical concepts and techniques to the study of the brain. Students will learn the fundamentals of signals and systems, pattern analysis, probability theory and information theories and apply these techniques to study how the real nervous systems computes, communicates and learns at many levels, from synapses to neurons, from neuronal populations to systems. Topics include basic anatomy and physiology of neurons and the mammalian nervous systems, biophysics of single neurons, excitable membranes and cable equation, encoding and decoding of information in single neurons and neuronal ensembles, neural adaptation and learning, signal detection and reconstruction, distributed and hierarchical computations. Concrete examples will be drawn from visual and motor systems and studied from both biological and computational perspectives. Students will do a number of Matlab programming and mathematical exercises to consolidate their learning. No prior background in biology is assumed.
Students will
| Instructors | Address | Phone | |
| Tai Sing Lee (Professor) | Mellon Inst. Rm 115 | tai@cs.cmu.edu | x8-1060 |
| Evaluation | % of Grade |
|---|---|
| Homework | 45 |
| Midterm exam | 10 |
| Term Project | 40 |
| Final Exam (optional for 15490, required for 15874) | 20 |
| Paper presentation | 5 |
You will have 1 to 2 weeks to do each homework programming assignment. The homework assignments are intended to introduce you to some basic techniques and to prepare you for the term project research. Homework report should be type-written if possible. You may collaborate with one partner on certain parts of the homework (with specific permission in the assignment), with explicit acknowledgement of your collaborator.
General guidelines: Each student is required to do an independent project. The project can be (1) an critical literature review of a particular problem, coupled with some simulation investigation; (2) a computational model to solve a particular problem; (3) a psychophysical experiment to test a model; (4) a study invovling the quantitative analysis of neural data. You are encouraged to work together the professor to come up with a specific project. You are expected to write up your results and experience in a paper and present orally to your classmates at the end of the semester.
| Date | Lecture Topic | Relevant Reading | Assignments |
|---|---|---|---|
| T 8/30 | 1. Introduction: the brain | ||
| R 9/1 | 2. Neurons | ||
| T 9/6 | 3. Spikes | ||
| R 9/8 | 4. Propagation | ||
| T 9/13 | 5. Synapses | ||
| R 9/15 | 6. Neuromuscular junction | ||
| T 9/20 | 7. Action | ||
| R 9/22 | 8. Sensors | ||
| T 9/27 | 9. Retina | ||
| R 9/29 | 10. Identification of RF | ||
| T 10/4 | 11. Filtering | ||
| R 10/6 | 12. Visual cortex | ||
| T 10/11 | 13. Frequency analysis | ||
| R 10/13 | 14. Dimensinal reduction (PCA) | ||
| T 10/18 | 15. Efficient codes (ICA) | ||
| R 10/20 | 16. Midterm and project proposals | ||
| M 10/24 | Midterm grade Due | ||
| T 10/25 | 17. Inference and decoding | ||
| R 10/27 | 18. Markov network | ||
| T 11/1 | 19. Grouping/binding | ||
| R 11/3 | 20. Belief propagation | ||
| T 11/8 | 21. Selective attention | ||
| R 11/11 | 22. Object recognition | ||
| T 11/15 | 23. Learning (Tom) | ||
| R 11/17 | 24. Adaptation | ||
| T 11/22 | 25. Hierarchical computation | ||
| R 11/24 | Thanksgiving Break | ||
| T 11/29 | 26. Reinforcement (Ryan) | ||
| R 12/1 | 27. Synchrony and coherency | ||
| T 12/6 | 28. Project Presention | FCE | |
| R 12/9 | 29. Project Presention | FCE | |
| S 12/11 | 30. Term project/paper due on line | ||
| 12/15-20 | Final Exam | ||
| R 12/22 | Final Grade Due |