Psy
5038W, Fall 2009, 3 credits
Psychology Department , University
of Minnesota
Place: S150 Elliott
Hall
Time: 9:45-11:00 MW
Course home pages: courses.kersten.org
Instructor: Daniel Kersten, Office:
S212 Elliott Hall, Phone: 625-2589, email: kersten@umn.edu
Office hours: Mondays 11:00 to 12:00 and by appointment.
TA: Michael Blank, Office:
email: blan0138@umn.edu
Office hours: 11:00-12:00 on Tuesdays, in Elliott Hall S501, and 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.
General Readings and Software
Grade Requirements
There will be a mid-term, final examination, programming assignments, as well as a final project. The grade weights are:
(NOTE:
Updated links to lecture material below will be revised and posted on the day of the lecture
--if
you want a preview, check
out lectures from 2007)
All lecture notes are in Mathematica Notebook and pdf format. You can download
the Mathematica notebook files below to view with Mathematica or MathPlayer
(which is free).
Date |
Lecture |
Additional Readings & supplementary material |
Assignments |
||
I.
|
1 |
Sep 9 |
Introduction (pdf file)|Mathematica notebook |
Mathematica screencast |
|
2 |
Sep 14 |
The
neuron (pdf
file)| Mathematica
notebook |
Hodgkin-Huxley.nb |
||
3 |
Sep 16 |
Neural Models, McCulloch-Pitt (pdf file)| Mathematica notebook | Koch, C., & Segev, I. (Eds.). (1998) (pdf) |
||
4 |
Sep 21 |
Generic neuron model (pdf file)| Mathematica notebook | |||
II. |
5 |
Sep 23 |
Lateral inhibition (pdf file)| Mathematica notebook | Hartline (1972) (pdf)
|
|
6 |
Sep 28 |
Matrices (pdf file)| Mathematica notebook | PS 1. Introduction to Mathematica , vectors, cross-correlation | ||
7 |
Sep 30 | Linear systems, learning & Memory (pdf file)| Mathematica notebook | |||
III. |
8 |
Oct |
Linear Associator (pdf file)| Mathematica notebook | ||
9 |
Oct 7 |
Sampling, Summed vector memory (pdf file)| Mathematica notebook | ProbabilityOverviewNN.nb | ||
10 |
Oct 12 |
Non-linear networks, Perceptron (pdf file)| Mathematica notebook |
|
PS 2. Lateral inhibition | |
11 |
Oct 14 |
Regression, Widrow-Hoff (pdf file)| Mathematica notebook | |||
12 |
Oct 19 |
Multilayer feedforward nets, Backpropagation (pdf file)| Mathematica notebook |
Poirazi,Brannon & Mel (2003) (pdf) Williams (1992) (pdf) |
||
IV.
|
13 |
Oct 21 |
Science
writing (pdf) (Mathematica notebook) |
Gopen & Swan,
1990 (pdf)
|
|
14 |
Oct 26 |
MID-TERM | MID-TERM (16%) | ||
15 |
Oct 28 |
Networks and Visual Representation (pdf file)| Mathematica notebook | Carrandini, Heeger, Movshon (1996)(pdf) | ||
16 |
Nov 2 |
Neural Representation and coding (pdf file) Mathematica notebook | Sanger (2003) (pdf) Quiroga, R. Q., Reddy, L., Kreiman, G., Koch, C., & Fried, I. (2005).(pdf) |
||
17 |
Nov 4 |
Self-organization, Principal Components Analysis (pdf file)| Mathematica notebook | Supplement: ContingentAdaptation.nb | ||
18 |
Nov |
Discrete Hopfield network (pdf file)| Mathematica notebook | |||
19 |
Nov 11 |
Graded response Hopfield network (pdf file)| Mathematica notebook | PROJECT IDEAS For demonstration style projects, see the Wolfram Demonstration site. |
||
20 |
Nov 16 |
Boltzmann machine (pdf file)| Mathematica notebook |
Sculpting the energy function, interpolation (pdf file)| Mathematica notebook) | ||
21 |
Nov 18 |
Adaptive maps (pdf file)| Mathematica notebook | smallRetinaCortexMap.nb GraylefteyeDan.jpg |
Final project title & paragraph outline (2%) |
|
22 | Nov 23 | Probability (pdf file)| Mathematica notebook |
Griffiths and Yuille (2006) (pdf) Jordan, M. I. and Bishop. C. MIT Artificial Intelligence Lab Memo 1562, March 1996. Neural networks. |
PS 4 Hopfield network | |
V.
|
23 | Nov 25 | More on neural coding, Generative
models,Bayes nets and inference (pdf file) Mathematica notebook |
Knill
& Pouget (2004) (pdf) Pouget et al. (2006) (pdf) |
|
24 | Nov 30 | Belief
Propagation (pdf) Mathematica notebook |
Pattern Recognition and Machine Learning, Chapter 8. Weiss Y. (pdf) |
||
25 | Dec 2 | EM (pdf) Mathematica notebook |
|||
26 | Dec 7 | Bayes decision theory (pdf) Mathematica notebook |
Fisher's linear discriminant notes (pdf) Mathematica notebook | REVISED DATES in RED Complete Draft of Final Project (5%) Due December 7 |
|
27 | Dec 9 | Kalman
filter, fisher discriminant (pdf) |
Kalman notes (pdf) |
Peer comments on Final Project (5%:) Due December 14 | |
28 | Dec 14 | SVMs, Bias/Variance, Wrap-up & Review |
Bias/Variance
notes (pdf) Mathematica SVMs: Nilsson, Björkegren & Tegnér (2006) |
Drafts returned with Instructor comments December 16 | |
Dec 16 | EXAM (offical last instruction day) | FINAL STUDY GUIDE | FINAL EXAM (16%) | ||
Dec 23 | (last day of fall semester) | Submit Final Revised Draft of Project (28%) | |||
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.
Some Resources:
Student Writing Support: Center for Writing, 306b Lind Hall and satellite locations
(612.625.1893) http://writing.umn.edu.
Online Writing Center:http://www.owc.umn.edu
NOTE: Plagiarism, a form of scholastic dishonesty and a disciplinaryoffense, is described by the Regents as follows: Scholasticdishonesty means plagiarizing; cheating on assignments or examinations;engaging in unauthorized collaboration on academic work; taking,acquiring, or using test materials without faculty permission; submittingfalse or incomplete records of academic achievement; acting alone or incooperation with another to falsify records or to obtain dishonestlygrades, honors, awards, or professional endorsement; or altering,forging, or misusing a University academic record; or fabricating orfalsifying of data, research procedures, or data analysis.http://www1.umn.edu/regents/policies/academic/StudentConductCode.html
© 2003, 2005, 2007 Computational Vision Lab, University of Minnesota, Department of Psychology.