September, 2003
Psy 5038W, Fall 2003, 3 credits
Psychology Department , University
of Minnesota
Place: 121 Elliott
Hall (Computer Lab)
Time: 12:00-1:30 TTh
Course home page:
courses.kersten.org
Instructor: Daniel Kersten Office:
212 Elliott Hall Phone: 625-2589 email: kersten@umn.edu
Office hours: Thursday 1:30-2:30 or by appointment.
TA: Bruce Hartung Office: N13 Elliott--Must
call: 625 1337 for access) email: hartung@cs.umn.edu
Office hours: Tuesday 1:15-2:15 or by appointment.
Course description. Introduction to large scale parallel distributed processing models in neural and cognitive science. Topics include: linear models, statistical pattern theory, Hebbian rules, self-organization, non-linear models, information optimization, and representation of neural information. Applications to sensory processing, perception, learning, and memory.
Readings
Grade Requirements
There will be a mid-term, final examination, programming assignments, as well as a final project. The grade weights are:
Assignment due BEFORE class start time (12:00 am) on the day due. You can use the downloaded Mathematica notebook for the assignment as your template, add your answers, and email your finished assignment to the TA. You can copy and paste any code bits you need from the Lecture notebooks. But of course, you cannot copy and paste code or any other answer materials from someone else.
(Revised lecture material will be posted on the day given--if you want a preview, check out lectures from 2001)
All lecture notes are in Mathematica Notebook and pdf format. You can download
the Mathematica notebook files below to view with Mathematica or MathReader
4 (which is free).
Date |
Lecture |
Additional Readings & supplementary material |
Assignments |
||
I.
|
1 |
Sep 2 |
Introduction (pdf file)|Mathematica notebook |
Mathematica
intro.nb Anderson: Intro. & Chapters 1, 2 |
|
2 |
Sep 4 |
The
neuron (pdf
file)| Mathematica
notebook |
|||
3 |
Sep 9 |
Neural Models, McCulloch-Pitt (pdf file)| Mathematica notebook | Jordan,
M. I. and Bishop. C. MIT Artificial Intelligence Lab Memo 1562, March
1996. Neural
networks. |
||
4 |
Sep 11 |
Generic neuron model (pdf file)| Mathematica notebook | |||
II. |
5 |
Sep 16 |
Lateral inhibition (pdf file)| Mathematica notebook | Anderson: Chapters 5, 6 & 7 | PS
1. Introduction to Mathematica
, vectors, cross-correlation (pdf file) |
6 |
Sep 18 |
Matrices (pdf file)| Mathematica notebook | |||
7 |
Sep 23 |
Learning & Memory (pdf file)| Mathematica notebook | Anderson: Chapter 8 | ||
III.
|
8 |
Sep 25 |
Linear Associator (pdf file)| Mathematica notebook |
einstein32x32.jpg |
|
9 |
Sep 30 |
Sampling, Summed vector memory (pdf file)| Mathematica notebook | (See first part of Lecture 23 for review of probability and statistics). | PS
2. Lateral inhibition - (pdf file) |
|
10 |
Oct 2 |
Non-linear networks, Perceptron (pdf file)| Mathematica notebook |
|
||
11 |
Oct 7 |
Regression, Widrow-Hoff (pdf file)| Mathematica notebook | Anderson: Chapter 9 | ||
12 |
Oct 9 |
Multilayer feedforward nets, Backpropagation (pdf file)| Mathematica notebook |
|
||
IV.
|
13 |
Oct 14 |
Science
writing (pdf) (Mathematica notebook) |
Gopen
& Swan, 1990
|
PS
3. Perceptron (pdf file) |
14 |
Oct 16 |
MID-TERM | MID-TERM (16%) | ||
15 |
Oct 21 |
Networks and Visual Representation (pdf file)| Mathematica notebook | Anderson: Chapters 10, 11 | ||
16 |
Oct 23 |
Neural Representation and coding (pdf file) Mathematica notebook | |||
17 |
Oct 28 |
Self-organization, Principal Components Analysis and NNs (pdf file)| Mathematica notebook | Anderson:
Chapter 12 (Supplement: ContingentAdaptation.nb) |
||
18 |
Oct 30 |
Discrete Hopfield network (pdf file)| Mathematica notebook | |||
19 |
Nov 4 |
Graded response Hopfield network (pdf file)| Mathematica notebook | |||
20 |
Nov 6 |
Boltzmann machine (pdf file)| Mathematica notebook |
PS
4 Backprop, Hopfield network (pdf file) |
||
21 |
Nov 11 |
Sculpting the energy function, interpolation (pdf file)| Mathematica notebook) | Anderson: Chapters 13, 14 | Final project title & paragraph outline (2%) | |
22 | Nov 13 | Adaptive maps (pdf file)| Mathematica notebook | Anderson:
Chapters 15, 16 smallRetinaCortexMap.nb GraylefteyeDan.jpg |
||
V.
|
23 | Nov 18 | Probability
(pdf file)| Mathematica notebook |
||
24 | Nov 20 | Generative
models,Bayes nets and inference (pdf file) Mathematica notebook |
|||
Nov 25 | Belief
Propagation (pdf) Mathematica notebook |
||||
Nov 27 | THANKSGIVING | ||||
26 | Dec 2 | EM (pdf) Mathematica notebook |
Complete Draft of Final Project (5%:) | ||
27 | Dec 4 | Bias/Variance (pdf) Mathematica notebook |
Bias/Variance notes (pdf) | ||
28 | Dec 9 |
Wrap-up & |
(Drafts returned) | ||
Dec 11 | FINAL EXAM | FINAL STUDY GUIDE | FINAL EXAM (16%) | ||
Dec 16 | Final Revised Draft of Project (33%) |
This course teaches you how to understand cognitive and perceptual aspects of brain processing in terms of computation. Writing a computer program encourages you to think clearly about the assumptions underlying a given theory. Getting a program to work, however, tests just one level of clear thinking. By writing about your work, you will learn to think through the broader implications of your final project, and to effectively communicate the rationale and results in words.
Your final project will involve: 1) a computer simulation and; 2) a 2000-3000 word final paper describing your simulation. For your computer project, you will do one of the following: 1) Devise a novel application for a neural network model studied in the course; 2) Write a program to simulate a model from the neural network literature ; 3) Design and program a method for solving some problem in perception, cognition or motor control. The results of your final project should be written up in the form of a short scientific paper, describing the motivation, methods, results, and interpretation. Your paper will be critiqued and returned for you to revise and resubmit in final form. You should write for an audience consisting of your class peers. You may elect to have your final paper published in the course's web-based electronic journal.
Completing the final paper involves 3 steps:
If you choose to write your program in Mathematica, your paper and program can be combined can be formated as a Mathematica notebook. See: Books and Tutorials on Notebooks.
Your paper will be critiqued and returned for you to revise and resubmit in final form. You should write for an audience consisting of your class peers.
© 1998, 1999, 2001, 2003 Computational Vision Lab, University of Minnesota, Department of Psychology.