NPB 163/PSC 128 - Information processing models


Date   Topic
W Jan. 7

Introduction.  What is a model?   What makes a good model?  Models in neuroscience. Matlab basics.

M Jan. 12
W Jan. 14

Linear systems.  Definition of a linear system; vectors and matrices; linear neuron models; receptive field models.
W Jan. 21
F  Jan. 23

Linear time-invariant systems.  Impulse reponse function; convolution; frequency response; RC-circuits.
M Jan. 26
W Jan. 28

Frequency analysis and auditory models.  Fourier transform; time-frequency analysis; spectro-temporal receptive fields; auditory scene analysis.
M Feb. 2
W Feb. 4

Supervised learning.   Adaptation in linear neurons;  Widrow-Hoff rule;  objective functions and gradient descent.
M Feb. 9
W Feb. 11

Unsupervised learning.   Linear Hebbian learning and PCA;  winner-take-all learning and clustering; sparse coding and ICA.
W Feb. 18

Plasticity and cortical maps.  Self-organizing maps; models of experience-dependent cortical re-organization.
M Feb. 23
W Feb. 25

Recurrent networks.  Hopfield networks; pattern completion; models of associative memory;  winner-take-all networks.
M Mar. 1
W Mar. 3

Probabilistic models and inference.  Probability theory;  generative models;  Bayesian inference;  perception as inference.
M Mar. 8
W Mar. 10

Neural coding and information theory.  Reverse correlation; Shannon's theory of information; efficient coding theories.

M Mar. 15
Spikes.  Integrate-and-fire model; neural encoding and decoding.