VS265: Neural Computation Fall2012

This is the Fall 2012 VS 265 Neural Computation course webpage.

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, sensorimotor loops, 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. This course was previously taught as VS298 (Fall 2006, Fall 2008) and VS265: Neural Computation Fall2010.

Instructors

Bruno Olshausen

• Email: link
• Office: 570 Evans
• Office hours: immediately following lecture

Mayur Mudigonda, GSI

• Email: lastname AT berkeley DOT edu (from above)
• Office: 567 Evans
• Office hours: 4-6 PM (Tu/Th) or by appointment

Lectures

• Location: 560 Evans (Redwood Center Conference Hall)
• Times: Mondays, Wednesdays - 9 to 10:30 AM
• Videos: graciously taped by our own from previous years Jeff Teeters.

Enrollment information

• Open to both undergraduate and graduate students, subject to background requirements specified below.
• Telebears: {CCN, Section, Units, Grade Option} == {66465, 01 LEC, 3, Letter Grade}

Email list and forum

• Please subscribe to the class email list here. The list name is vs265-students.

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. Additional reading, such as primary source material, will be suggested on a lecture by lecture basis.