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

Tai Sing Lee (Professor) | GHC 8017 (M 3-4:40) and MI 115 (Thursday/Friday by appointment) | tai@cnbc.cmu.edu (412-268-1060) |

Dan Howarth (TA) | GHC 4122 (Tu 4:30-6:00) and MI 116E (Fri 12:45-2:15) | d.c.howarth@gmail.com |

**Class location and time:**GHC 4211 Monday/Wednesday 1:30 p.m - 2:50 p.m.**Website:**http://www.cnbc.cmu.edu/~tai/nc-14.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 additional readings are provided in 386 BB but labeled as such.

- 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 (reference).

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

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

Assignments | 60 |

Quiz 1 | 10 |

Final Exam and/or Quiz 2 series * | 30 |

Term project (optional 386, required for 686) | 15 |

- 5 Matlab programming or 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 one quiz or assignment grade, and/or to contribute to up to 50% of the final exam grade. Collaboration is allowed, but in this case, it can't be used to replace homework or quiz grades, but can contribute to the final, i.e. the final exam will only count for 15 points for these students. 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. Collaboration is allowed. The maximum project grade will be 15 points. The grade will be assigned based on ranking of all the projects, with the best grade being 100% and the worst grade being 80% of the maximum grade, uniformly scaled among all projects.

- 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 % (same as 386) and the top 2/3 of the term projects.

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

LEARNING AND REPRSENTATION | |||

M 1/13 | 1. Introduction | ||

W 1/15 | 2. Biology of Neurons | ||

M 1/20 | 3. No class | ||

W 1/22 | 4. Spikes | HW 1 out | |

F 1/24 | A. Matlab tutorial | ||

M 1/27 | A. Matlab tutorial | ||

W 1/29 | 5. Neural Codes | ||

F 1/31 | 6. Precision and stochasticity | ||

M 2/3 | 7. Synapses and Plasticity | HW 1 in, HW 2 out | |

W 2/5 | 8. Hebbian learning | ||

M 2/10 | 9. Neural Tuning | ||

W 2/12 | 10. Linear/Nonlinear System | ||

M 2/17 | 11. Efficient coding | ||

W 2/19 | 12 Competitive learning | HW 2 in, HW 3 out. | |

M 2/24 | 13. Computational maps | ||

W 2/26 | 14. Associative learning | ||

M 3/3 | 15. Deep learning | ||

W 3/5 | Midterm | HW 3 in, HW 4 out | |

F 3/8 | Midterm Grade | ||

M 3/10 | Spring break | ||

W 3/12 | Spring break | ||

INFERENCE AND DECODING | |||

M 3/17 | 16. Memory and Hippocampus | Project proposal due | |

W 3/19 | 17. Bayesian decoding | HW 3 due, HW 4 out | |

M 3/24 | 18. Motor BMI | ||

W 3/26 | 19. Perception inference | ||

M 3/31 | 20. Recogniton and Classification | ||

W 4/2 | 21. Recurrent computation | HW 4 due. Hw 5 out | |

M 4/7 | 22. Semantic networks | ||

W 4/9 | 23. Current Research | ||

F 4/11 | Spring carival | ||

M 4/14 | 24. Cognitive modeling ART | ||

W 4/16 | 25. Concept modeling | HW 5 due | |

M 4/21 | 26. Attention and Prediction | ||

W 4/23 | 27. Review and Open Questions | ||

M 4/28 | 28. Project Presentation | ||

W 4/30 | 29. Project Presentation | Term paper due | |

R 5/15 | Final Grade due 4 p.m. |

Questions or comments: contact Tai Sing Lee

Last modified: Jan 9, 2014, Tai Sing Lee