VS298: Neural Computation: Difference between revisions
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'''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. | '''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. | ||
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Revision as of 00:52, 18 August 2008
People
Professor: Bruno Olshausen
- Email: baolshausen AT berkeley DOT edu
- Office: 570 Evans
- Office hours: TBD
GSI: Amir Khosrowshahi
- Email: amirk AT berkeley DOT edu
- Office: 567 Evans
- Office hours: TBD
Course description
This is a 3-unit course that 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.
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 will be 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.
Lectures
- Location: Evans 508-20
- Times: TBD
- Telebears: {CCN, Section, Units, Grade Option} == {TBD, 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, ideally 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.
