VS298: Neural Computation
Course description
This course provides an introduction to the theory of neural computation. The goal is to familiarize students with the major theoretical frameworks and models used in neuroscience and psychology, and to provide hands-on experience in using these models. Topics include neural network models, supervised and unsupervised learning rules, associative memory models, probabilistic/graphical models, and models of neural coding in the brain.
This course differs from MCB 262, Advanced Topics in Systems Neuroscience, in that it emphasizes the theoretical underpinnings of models - i.e., their mathematical and computational properties - rather than their application to the analysis of neuroscientific data. It is offered in alternate years, interleaving with MCB 262. Students interested in computational neuroscience are encouraged to take both of these courses as they complement each other.
Instructors
- Email: baolshausen AT berkeley DOT edu
- Office: 570 Evans
- Office hours: TBD
Amir Khosrowshahi, GSI
- Email: amirk AT berkeley DOT edu
- Office: 567 Evans
- Office hours: TBD
Lectures
- Location: Evans 508-20
- Times: 3 hours per week, to be determined at organizational meeting on Tuesday, August 26 at 5:00. You can also indicate your preferences by signing up here.
Enrollment information
- Open to both undergraduate and graduate students, subject to background requirements specified below.
- Telebears: {CCN, Section, Units, Grade Option} == {66487, 02 LEC, 3, Letter Grade}
Email list and forum
- Please subscribe to the class email list here. The list name is vs298-students.
- A bulletin board is provided here for discussion regarding lecture material, readings, and problem sets. Signup required for posting.
Grading
Based on weekly homework assignments (60%) and a final project (40%).
Required background
Prerequisites are calculus, ordinary differential equations, basic probability and statistics, and linear algebra. Familiarity with programming in a high level language such as Matlab is also required.
Textbooks
- [HKP] Hertz, J. and Krogh, A. and Palmer, R.G. Introduction to the theory of neural computation. Amazon
- [DJCM] MacKay, D.J.C. Information Theory, Inference and Learning Algorithms. Available online or Amazon
- [DA] Dayan, P. and Abbott, L.F. Theoretical neuroscience: computational and mathematical modeling of neural systems. Amazon
HKP and DA are available as paperback. Some copies of HKP and DJCM are available at the Berkeley bookstore. Additional reading, such as primary source material, will be suggested on a lecture by lecture basis.
