Topics in Theoretical and Computational Neuroscience
Time: Tue,
Thu
Room: 2129 Tolman
Class ID: MCB262
Semester: Fall 2003
Last Updated:
Submit your homework electronically
here.
Instructors
UCB Faculty:
Yang Dan, Frédéric Theunissen,
RNI Visiting Faculty: Bill Softky, Bruno Olshausen, Tony Bell, Fritz Sommer
UCB Reader: Patrick Gill.
Class Description
The class will cover analytical methods used in systems and cognitive neuroscience including systems analysis, information theory and neural networks. The application of these methods will be illustrated with examples in vision, audition and neural plasticity. The class consists of one hour and half lecture on Thursday and a student led discussion section on Tuesday.
Assignments
The class is organized in sessions. For each session, there is a background reading assignment, a paper assignment and a homework. The background reading must be completed before the lecture on Thursday. The paper reading must be done for the Tuesday of the following week. The homework will be posted on the Web by the lecture day and is due on the following on Thursday (1 week later). Each student must also volunteer to lead the discussion for one of the Tuesday sections. The homework will emphasize the mathematical aspects of the material and the discussion will emphasize the application of the computational technique in Neuroscience. The homeworks will involve Matlab programming. The grade will be based on the homework and class participation.
Required Textbook:
Theoretical Neuroscience. P.
Dayan and L Abbott. MIT Press 2001.. Background
Reader
Syllabus
|
Session |
Date |
Lecturer Organizer |
Topic |
|
1 |
8/28 9/2 |
Softky Dan |
Introduction, spike trains, firing rates, what makes a neuron fire |
|
2 |
9/4 9/9 |
Olshausen Theunissen |
Basic linear systems analysis, Fourier transform, power spectrum, correlation, convolution, etc. |
|
3 |
9/11 9/16 |
Theunissen |
Advanced linear systems analysis, non-white inputs, multi-dimensional inputs |
|
4 |
9/18 9/23 |
Dan |
Early visual processing, Receptive fields of simple and
complex cells |
|
5 |
9/25 9/30 |
Theunissen |
Auditory processing, characterization of STRF Background Reading Paper Homework |
|
6 |
10/2 10/7 |
Theunissen |
Neural code & information theory I |
|
7 |
10/9 10/14 |
Gastpar Theunissen |
Neural code & information theory II |
|
8 |
10/16 10/21 |
Softky Theunissen |
Modeling single neurons |
|
9 |
10/23 10/28 |
Theunissen |
Circuit model: Auditory processing. Models for Pitch and Timbre. |
|
10 |
10/30 11/4 |
Dan |
Circuit model: V1 processing of orientation |
|
11 |
11/6 11/18 |
Dan |
Plasticity in sensory systems Background Reading Paper No Homework (Note that we are skipping a week between the lecture and discussion for Neuroscience. |
|
12 |
11/20 11/25 |
Sommer Dan |
Hebbian
learning, persistent activity and associative memory Background Reading Paper Homework |
|
|
11/27 12/2 |
|
No class, Thanksgiving
Holiday |
|
13 |
12/4 12/9 |
Olshausen Dan |
Neural network learning Background Reading Paper No Homework |
Office Hours and E-mails
E-Mail to Students in the Class
Frederic E Theunissen OH: M 10-11 3425 Tolman
Yang Dan OH: TBA 195 LSA
Michael Gastpar OH: W 11-12 Th 2-3 265 Cory
Patrick Gill OH: M 4-5 3108 Tolman Notes
Other Resources
Notes from lecture 4 (1.35 MB PowerPoint)
Notes from lecture 10 (4 MB PowerPoint)
Computational Neuroscience Textbooks
- Spikes by Rieke et Al.
- Methods in Computational Neuroscience.
Web sites
Redwood Neuroscience Institute