This is a one-credit course that will meet for 5 weeks, two hours per
session. This course will explore connectionist (or Parallel
Distributed Processing, or artificial Neural network) models and their
relation to cognitive processes. We will cover what neural networks
are, their use and current learning methods for them. We will also look
at the application of these models to several problems in cognitive
modeling. The use of demonstration programs will be an integral part of
the course. Each week I will try to limit myself to talking for one
hour, and then we will "play" on the computer for an hour.
Week 1 will motivate why we are interested in neural networks as
of cognition. We will also try out a simple model or two on the
computer, using the cs program. Week 2 will cover more models,
especially so-called "attractor networks" as models of memory, using
the Jets & the Sharks example (iac program). Week 3 will introduce
learning using perceptrons and back propagation, using the bp program.
In week 4, we will use a more modern implementation of neural
networks and see how we can train networks to recognize facial
expressions. Week 5 I will give a talk on my own research, and briefly
go over why neural nets are so "hot" right now - because they are the
basis for Deep Learning. We will see some demos of what can be done
with Deep Learning on the web.
The only required work consists of attending class each
week. I will actually take attendance. If you
miss one class with a good excuse, that’s ok. If you miss two classes,
you will receive an NP, unless you write a five-page paper covering
some aspect of neural networks of interest to you. This may be a
"thought piece", suggesting how neural nets may explain some aspect of
human cognition, or a simple regurgitation of some of the things you
learned in the course, or a write-up of a (very small!) project using
the PDP simulation software (see the "Explorations" book below).
Some of the slides I present are available here:
- First day lecture is here: [pptx] [pdf]
- Second day lecture is here: [pptx] [pdf]
- Third day lecture is here: [pptx] [pdf]
- Fourth day lecture is here: [NONE: We just ran the simulations...]
- Last day lectures...still to come!
Explorations in Parallel Distributed Processing,
McClelland & Rumelhart, MIT Press, 1988. This book is a self-paced
exploration using running programs of cognitive models. All of the
simulators are on your machines right now. Also recommended is Parallel Distributed Processing
, edited by Rumelhart and McClelland. MIT Press, 1986.
These books are ones you will want to have if you plan on eventually
doing research in Cognitive Science.
All three of these books are available free online as downloadable pdf’s on Jay McClelland’s website
. In particular, much of the class is inspired by this chapter
. Here is the chapter on backpropagation learning
First day computer instructions:
- Log in to windows (not Unix!) using your UCSD email as your login and your email password.
- Install PDPTool using the following steps:
- Click on pdptool.zip to download it to your desktop.
- Extract the archived files into a new folder called "pdptool". Usually this can be done by right-clicking on the file..
- Start Matlab.
- Within Matlab, set your path variable to point to PDPTool using the following steps:
some matlabs, from the File menu, select Set path. A dialog box opens.
OR, in some matlabs, look for the "set path" button about 4/5 of the
way across the page at the top. Click on it.
- Click the "Add with subfolders" button. A directory browser window opens.
- Locate the folder called "pdptool". Select it and click OK.
- Click the "Save" button on the dialog box to save the path for future sessions.
- Click the "Close" button.
- Set your command history preferences using the following steps:
- From the File menu, select "Preferences". A dialog box opens. OR, look for the "Preferences" button above the "Set Path" button.
- Select "Command History" from the list of options on the left. This displays the current command history settings.
- In the "Saving" section of the history settings, select "Save after [n] commands", where [n] is a numerical field.
- Change [n] to 1.
- Click "OK".
- At the Matlab command prompt, type "pdp" to start the program.
- In order to run the Necker Cube example, type "cube"