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

Tai Sing Lee (Professor) | MI 115 Friday 1:30-2:30 | tai@cnbc.cmu.edu (412-268-1060) |

Yimeng Zhang (TA) | Monday 6-7 pm Citadel teaching area, GHC 5th floor | yimengzh@cs.cmu.edu |

Jeff Helt (1/2 TA) | Tuesday 6:30-7:30 Citadel teaching area, GHC 5th floor | jhelt@andrew.cmu.edu |

Shefali Umrania (1/2 TA) | Tuesday 6:30-7:30 Citadel teaching area, GHC 5th fllor | sumrania@andrew.cmu.edu |

**Class location and time:**GHC 4211 Monday/Wednesday 1:30 p.m - 2:50 p.m.**Website:**http://www.cnbc.cmu.edu/~tai/nc17.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 | 70 |

Quiz 1 | 10 |

Final Exam or Quiz 2 | 20 |

Term project (required for 686) | 15 |

- 6 Matlab programming and mathematical assignments on dynamical models of neurons, receptive learning, neural networks, neural decoding, deep belief nets. 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.

- A 386 student can do the term project to replace one quiz or assignment grade. 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.

- The midterm 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 a total of 5 days grace period to turn in one of five homework assignments late without penalty. These five days could be for one assignment (for 5 days) or distributed among assignments, Blackboard submission after noon on the due day is considered late.
- If you need to submit a late homework after using the free late days, 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 % plus a respectable term project.

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

LEARNING AND REPRSENTATION | |||

W 1/18 | 1. Introduction | ||

M 1/23 | 2. Neurons | HW 1 out; | |

W 1/25 | 3. Spikes | ||

M 1/30 | 4. Matlab tutorial (guest) | ||

W 2/1 | 5. Synapse and plasticity | HW 1 in | |

M 2/6 | 6. Hebbian learning | ||

W 2/8 | 7. System Analysis | HW 2 out | |

M 2/13 | 8. Neural codes | ||

W 2/15 | 9. Sparse coding | ||

M 2/20 | 10. Competitive learning | ||

W 2/22 | 11. Map learning | HW 2 in, HW 3 out. | |

M 2/27 | 12. Perceptron | ||

W 3/1 | 13. Hierarchy | ||

M 3/6 | 14. Midterm (guest) | ||

W 3/8 | 15. Deep networks | HW 4 out. | |

M 3/13 | Midterm grade, Spring break | ||

W 3/15 | Spring break | ||

ASSOCIATION and INTERACTION | |||

M 3/20 | 16. Perceptual inference | HW 3 due | |

W 3/22 | 17. Decision making | ||

M 3/27 | 18. Memories and imagination | ||

W 3/29 | 19. Mind reading | HW 4 in. HW 5 out | |

M 4/3 | 20. Associative learning | ||

W 4/5 | 21. Recurrent networks (generative models) | ||

M 4/10 | 22. Ensemble codes (correlation) | ||

W 4/12 | 23. Brain Networks and States | HW 5 in, HW 6 out. | |

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

W 4/19 | 25. Predictive Network | ||

M 4/24 | 26. Reinforcement Learning | ||

W 4/26 | 27. Motor System and BCI | HW 6 in. | |

M 5/1 | 28. Neurally-inspired intelligence | ||

W 5/3 | 29. Review /project presentation | Term paper | |

R 5/23 | Final Grade due 4 p.m. |

Questions or comments: contact Tai Sing Lee

Last modified: Jan 12, 2017, Tai Sing Lee