Block Practical: Connectionist Models and Cognitive Processes

Course Materials

1st Week: Introduction to Programming and Connectionist Networks

• Code: pyceptron.py
• Slides: Part 1: Intro to Programming, Part 2: Intro to Networks
• Exercises: Pyceptron

2nd Week: Going from Two Network Layers to Three

• Code: network_missing.py, network_hints.py, network.py
• Slides: Part 3: Feedfoward Networks
• Exercises: Backpropagation

3rd Week: Replicating a Model

• Code: network.py
• Patterns: tyler_patterns.csv
• Slides: Part 4: Replicating a Model
• Exercises: Replication of Tyler et al. (2000)
• Tyler, L. K., Moss, H. E., Durrant-Peatfield, M. R., & Levy, J. P. (2000). Conceptual structure and the structure of concepts: A distributed account of category-specific deficits. Brain and Language, 75(2), 195-231.

4th Week: Writing up Experimental Results

• Code: network.py, graph.py
• Example file for errors: errors1000.txt
• Slides: Part 5: Writing the Report
• Exercises: File Input/Output

Reading Materials

• Essay: A Brief Introduction to Connectionism

Programming

Exercises

• Codecademy
• LearnPython.org
• Codewars
• Code School: Python

Books

• Learn Python the Hard Way
• How to Think Like a Computer Scientist: Learning with Python
• Think Python: How to Think Like a Computer Scientist

Inspiration

Libraries

• Numpy Tutorial
• Matplotlib Examples
• A Primer on Scientific Programming with Python
• Scipy Lecture Notes

Blogs

• The Glowing Python: This blog has various examples of interesting code to play with and give you ideas for your own projects.
• WildML: Recurrent Neural Networks Tutorial, Part 1 – Introduction to RNNs: This blog also has other Machine Learning tutorials.

Video Lectures

• Machine Learning, by The Royal Society
• The Cognitive and Computational Neuroscience of Categorization, Novelty-Detection, and the Neural Representation of Similarity, by Mark Gluck

Online Courses

• Machine Learning, by Andrew Ng
• Neural Networks for Machine Learning, by Geoffrey Hinton
• Introduction to Neural Networks, by Sebastian Seung

How to install Python

Windows Users

This is a little tricky:

1. Install Python: download from here

2. Install matplotlib, numpy, and scipy using pip. Specifically you need to download the following from here:

• matplotlib-1.4.3-cp27-none-win32.whl
• numpy-1.10.0b1+mkl-cp27-none-win32.whl
• scipy-0.16.0-cp27-none-win32.whl

This requires you to be in the Scripts folder of the Python27 installation. And to use the windows command prompt. For me this looks like:

C:\Python27\Scripts>pip install NAME_OF_WHEEL_FILE.whl

For all three of those you need to run a pip command like above.

3. Install PyGTK: download from here

4. To check that everything works, open network.py and see if it runs without any errors.

Mac Users

I finally managed to do this on my mac. Use Homebrew to install matplotlib, numpy, scipy, pygtk.

Linux Users

Use your favourite package manager to install matplotlib, numpy, scipy, pygtk.