Instructors | Office (Office hours) | Email (Phone) |
---|---|---|

Tai Sing Lee (Professor) | GHC 8017 (Tuesday 1:45-2:45 p.m.) also after each class | tai@cnbc.cmu.edu (412-268-1060) |

Andrew Noh (TA) | HH 1st floor study rooms near the 1300 corridor (MW 3-4:30) | anoh@andrew.cmu.edu |

**Class location and time:**Wean 5415 Tuesday/Thursday 3:00 p.m - 4:30 p.m.**Website:**http://www.cnbc.cmu.edu/~tai/nc-12.html (course info)**Blackboard:**http://www.cmu.edu/blackboard/ (Both 386/686 students should use 386 BB for access of course materials and announcements. 686 students should also check their 686 BB for additional readings

- Handouts in Blackboard.
- Trappenberg T.P. (TTP)
*Fundamentals of computational neuroscience*, 2nd edition, Oxford University Press 2009 (required/recommended). - Hertz J, Krogh A, Palmer RG (HKP)
*Introduction to the theory of neural computation.*, Addison Wesley 1991 (recommended).

- Turn OFF your laptop, cell phones or any other electronic devices in the classroom.

Evaluation | % of Grade |
---|---|

Assignments | 50 |

Quiz 1 | 10 |

Quiz 2 | 10 |

Final Exam | 30 |

Term project (optional) | 17 |

686 Term Project | Required |

- 4 or 5 programming and mathematical assignments. The solution should be in pdf file, and should be submitted before class to blackboard.
- Collaboration in team of two is allowed for the first two assignments.

- Term project is optional for 386 students. A 386 student can use the OPTIONAL term project to replace quiz/assignment grades, and/or to contribute to the final exam grade. The maximum grade for the term project is 17 points. That is, it worths up to 85 percent of the 20 points in the assignments/quiz/final grades. Collaboration is allowed, but in this case, the maximum grade worths up to 75 percent of 20 points, i.e. 15 points each person. It will require a 6 pages written final report and a presentation to the class. Matlab codes and additional output should also be submitted as supplementary materials in a different pdf/doc file and/or matlab zip files.
- Term project is required of 686 students, and will be graded in percentile according to written report and presentation. Collaboration is allowed. Top 65 percent term project performance (relative to all (386/686) students' term projects) (in addition to achieving 88 percent in the other scores) is required for an A. Students are encouraged to work on issues that are not covered in the course.

- The quizzes and final exam will cover materials covered in lectures. Students are encouraged to study together for these exams.

- You will have about 2 weeks to do each homework assignment. Homework report should be type-written. You should submit a hardcopy, documenting all the answers and results in class on the due day, as well as a softcopy of write-up and the codes to Blackboard before noon on the due day. Blackboard submission after noon is considered late.
- All students are allowed to collaborate in the FIRST two assignments ONLY to facilitate matlab learning. Each should submit a copy of the solution, but should list his/her partner, if any, on the first page of the report.
- Each student has ONE chance to turn in one of five homework assignments late within one week without penalty. However, Blackboard submission after noon on the due day is considered late.
- If you need to submit a late homework after using the one free late, you will receive 5 percent deduction from maximum credit per lecture the solution set is released.
- No extension possible for the term project.
- 386 students: A for total grade >= 88 %, B: < 88 AND >= 75, C: < 75, >=60. F for cheating.
- 686 students: A for total grade >= 88 % and top 65 term project performance.

Date | Lecture Topic | Relevant Readings | Assignments |
---|---|---|---|

LEARNING AND REPRSENTATION | |||

T 1/17 | 1. Introduction | ch 1 | |

R 1/19 | 2. Neurons and spikes | ||

T 1/24 | 3. Sounds to spikes | ||

R 1/26 | 4. Cochlear coding | ||

T 1/31 | 5. Synapses and transmission | HW 1 out | |

W 2/1 | Matlab tutorial /Help : 5:30-7:00 | ||

R 2/2 | 6. Precion and Stochasticity | ||

T 2/7 | 7. Hebbian learning | Help session | |

R 2/9 | 8. Neural Tuning | HW 1 due. HW 2 out; | |

T 2/14 | 9. Linear Transforms | ||

R 2/16 | 10. Redundancy reduction | ||

T 2/21 | 11 Visual Representation | ||

R 2/23 | 12. Sparse coding | HW 2 due. HW 3 out | |

T 2/28 | 13. Competitive Learning | ||

R 3/1 | 14. Computational map | ||

T 3/6 | Quiz 1 | ||

R 3/8 | 15. Assocative Learning | Project Proposals | |

F 3/9 | Midterm Grade due 6 p.m. | ||

T 3/13 | Spring break | ||

R 3/15 | Spring break | ||

INFERENCE AND DECODING | |||

T 3/20 | 16. Bayesian inference | ||

R 3/22 | 17. Memory and hippocampus | HW 3 due, HW 4 out | |

T 3/27 | 18. Motor decoding and control | ||

R 3/29 | 19. Perception inference | ||

T 4/3 | 20. Neural implementation | ||

R 4/5 | 21. Supervised Learning | HW 4 due. Hw 5 out | |

T 4/10 | 22. Semantic network | ||

R 4/12 | 23. Emotion decoding | ||

T 4/17 | 24. Concept learning | ||

R 4/19 | Spring Carnival | ||

T 4/24 | 25. Quiz 2 | ||

R 4/26 | 26. Course review | HW 5 due | |

T 5/1 | 27. Project Presentation | ||

R 5/3 | 28. Project Presentation | Term paper due | |

R 5/17 | Final Grade due 6 p.m. |

Questions or comments: contact Tai Sing Lee

Last modified: Jan 9, 2012, Tai Sing Lee