This course will introduce basic principles of neural networks in relation to human cognition with applied practical programming of simple neural networks. Students will read three modeling papers and apply the neural network models in these papers to create their own neural networks. Three examples of networks will be covered: 1) Attractors (Hopfield, 1982), 2) Backpropagation (Perceptron; Rumelhart et al., 1986), 3) Unsupervised Learning (Von Der Malsburg, 1973).