Course page

VS265: 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, recurrent networks, probabilistic/graphical models, and models of neural coding in the brain.

This course was previously taught as VS298 (Fall 2006Fall 2008), VS265: Neural Computation Fall2010, and VS265: Neural Computation Fall2012.


Bruno Olshausen

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

Brian Cheung, Mayur Mudigonda, GSI’s

  • Email: bcheung, mudigonda (respectively) AT berkeley DOT edu
  • Office: 567 Evans
  • Office hours: TBD


  • Location: 560 Evans (Redwood Center conference room)
  • Times: Tuesday, Thursday – 3:30 to 5 PM
  • 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} == {66471, 01 LEC, 3, Letter Grade}

Email list and forum

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


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.


  • [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.