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 Introduction to Neural Networks  posted by  member7_php   on 2/12/2009  Add Courseware to favorites Add To Favorites  
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Seung, Sebastian, 9.641J Introduction to Neural Networks, Spring 2005. (Massachusetts Institute of Technology: MIT OpenCourseWare),  (Accessed 09 Jul, 2010). License: Creative Commons BY-NC-SA

Spring 2005

Neurons forming a network in disassociated cell culture. (Image courtesy of Seung Laboratory, MIT Department of Brain and Cognitive Sciences.)

Course Highlights

This course features a selection of downloadable lecture notes and problem sets in the assignments section.

Course Description

This course explores the organization of synaptic connectivity as the basis of neural computation and learning. Perceptrons and dynamical theories of recurrent networks including amplifiers, attractors, and hybrid computation are covered. Additional topics include backpropagation and Hebbian learning, as well as models of perception, motor control, memory, and neural development.

Technical Requirements

Special software is required to use some of the files in this course: .mat, and .m.

*Some translations represent previous versions of courses.


Course Philosophy

The subject will focus on basic mathematical concepts for understanding nonlinearity and feedback in neural networks, with examples drawn from both neurobiology and computer science. Most of the subject is devoted to recurrent networks, because recurrent feedback loops dominate the synaptic connectivity of the brain. There will be some discussion of statistical pattern recognition, but less than in the past, because this perspective is now covered in Machine Learning and Neural Networks. Instead the connections to dynamical systems theory will be emphasized.

Modern research in theoretical neuroscience can be divided into three categories: cellular biophysics, network dynamics, and statistical analysis of neurobiological data. This subject is about the dynamics of networks, but excludes the biophysics of single neurons, which will be taught in 9.29J, Introduction to Computational Neuroscience.


  • Permission of the instructor
  • Familiarity with linear algebra, multivariate calculus, and probability theory
  • Knowledge of a programming language (MATLAB® recommended)

Course Requirements

  • Problem sets
  • Midterm exam
  • Final exam


The following text is recommended:

Hertz, John, Anders Krogh, and Richard G. Palmer. Introduction to the Theory of Neural Computation. Redwood City, CA: Addison-Wesley Pub. Co., 1991. ISBN: 9780201515602.


Lec # Topics Key DATES
1 From Spikes to Rates  
2 Perceptrons: Simple and Multilayer  
3 Perceptrons as Models of Vision  
4 Linear Networks Problem set 1 due
5 Retina  
6 Lateral Inhibition and Feature Selectivity Problem set 2 due
7 Objectives and Optimization Problem set 3 due
8 Hybrid Analog-Digital Computation
Ring Network
9 Constraint Satisfaction Stereopsis Problem set 4 due
10 Bidirectional Perception  
11 Signal Reconstruction Problem set 5 due
12 Hamiltonian Dynamics  
13 Antisymmetric Networks  
14 Excitatory-Inhibitory Networks Learning  
15 Associative Memory  
16 Models of Delay Activity Integrators Problem set 6 due one day after Lec #16
17 Multistability Clustering  
18 VQ PCA Problem set 7 due
19 More PCA Delta Rule Problem set 8 due
20 Conditioning Backpropagation  
21 More Backpropagation Problem set 9 due
22 Stochastic Gradient Descent  
23 Reinforcement Learning Problem set 10 due
24 More Reinforcement Learning  
25 Final Review  
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