Introduction to canonical neural network models of cognition

About

The goal of this project is to introduce a selection of canonical neural network models of cognition. Each section covers the following contents:

• Historical and theoretical background
• Mathematical formalization
• Code implementation in Python
• Example application
• Model limitations

Models are implemented in Python as Jupyter Notebooks tutorials. Although is recommended to follow the tutorials in a linear fashion, they can be used as stand-alone learning material.

The tutorials are intended to be used by beginner to intermediate level students and/or researchers in cognitive science or related fields, for instance, advance undergraduates or early-stage graduate students. Knowledge of Python is not required, but it is advised to have previous exposure/experience working with some dynamically typed programming language like R, Julia, Scala, or Matlab.

Contents

1. Introduction to Computational Models of Cognition
2. Roadmap to Neural Network Models of Cognition
3. The McCulloch-Pitts Artificial Neuron (McCulloch & Pitts, 1943)
4. The Perceptron (Rossenblat, 1958) ​
5. The Adaline - Adaptive Linear Neuron (Widrow & Hoff, 1959)
6. The Multilayer Perceptron (Rummelhart, Hinton & Williams, 1986)
7. The Convolutional Neural Network (Lecun et all, 1989; LeCun et all 1998, Krizhevsky et all, 2012)
8. The Recurrent Neural Network (Lecun et all, 1989; LeCun et all 1998, Krizhevsky et all, 2012)

Requirements

To set up your machine, you first need this dependencies:

• python==3.6
• git= 2.17.1
• pip >= 20.0.2

Usage

Remote

Click in the binder icon ->

Locally

To run the notebooks in your machine, open the terminal and follow these steps:

# Step 0: check python 3.6.x
python --version
# if necessary, install the required python version
# consider using pyenv to manage and switch python versions safely

# Step 1: get the repository
git clone https://github.com/pabloinsente/nn-mod-cog

# Step 2: navigate into the directory
cd nn-mod-cog/

# Step 3: create virtual environment 
python3 -m venv venv

# Step 4: activate virtual environment 
source venv/bin/activate

# Step 5: check pip version >= 20.0.2
pip --version

# upgrade if necessary with
pip install --upgrade pip

# Step 6: install dependencies
pip install -r requirements.txt

# Step 7: Run Jupyter Lab
jupyter lab ./notebooks.