NPB 163 / PSC 128 - Winter 2004

Information processing models in neuroscience and psychology

MW 12:10-2, Wellman 212

Instructor:    Bruno Olshausen 
Reader:    Mike Cohen


Course information

Lecture schedule

Readings and lecture notes

Intro lecture slides

Matlab intro

Linear algebra

Linear neuron

Receptive field models

Linear, time-invariant systems


Simulating differential equations

Fourier analysis

Supervised learning I

Supervised learning II

Linear Hebbian learning and PCA

Attractor neural networks

A probability primer

Bayesian inference

The mixture of Gaussians model

Information theory


Lab #1 - Matlab

Lab #2 - Model fitting and linear systems

Lab #3 - Cell membrane model

Lab #4 - Frequency analysis

Lab #5 - Sound synthesis

Lab #6 - Supervised learning

Lab #7 - Unsupervised learning

Lab #8 - Competitive learning, Kohonen nets, Hopfield nets

Lab #9 - The mixture of Gaussians model