HOME MISSION AND RESEARCH PUBLICATIONS HISTORY PEOPLE SEMINARS COURSES VIDEO ARCHIVE CONTACT

VS298 (Fall 06): Neural Computation

From RedwoodCenter

Revision as of 05:59, 17 August 2008 by Amir (Talk | contribs)

(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)

People

Professor: Bruno Olshausen

  • Email: baolshausen AT berkeley DOT edu
  • Office: 10 Giannini, 3-1472
  • Office hours: TBD

GSI: Amir Khosrowshahi

  • Email: amirk AT berkeley DOT edu
  • Office: 523 Minor, 3-5996
  • Office hours: Wednesday 1:30-2:30

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: Wednesday: 489 Minor Hall, Friday: 100 Minor Hall
  • Times: WF 2:30-4:00, starting Wednesday, Sept 6th.
  • Telebears: {CCN, Section, Units, Grade Option} == {66484, 02 LEC, 3, Letter Grade}

Email list and forum

  • Please subscribe to the class email list here. The list name is vs298-students. Alternatively, please email the GSI.
  • 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.

Reading

Class schedule

Lecture slides

Additional resources

Homework

Suggested projects

Syllabus

Personal tools