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 timeinvariant systems. Impulse reponse function;
convolution; frequency response; RCcircuits. 
M Jan. 26 W Jan. 28 
Frequency analysis and auditory models. Fourier transform;
timefrequency analysis; spectrotemporal receptive fields; auditory scene
analysis. 
M Feb. 2 W Feb. 4 
Supervised learning. Adaptation in linear neurons;
WidrowHoff rule; objective functions and gradient descent. 
M Feb. 9 W Feb. 11 
Unsupervised learning. Linear Hebbian learning
and PCA; winnertakeall learning and clustering; sparse coding and
ICA. 
W Feb. 18 
Plasticity and cortical maps.
Selforganizing maps; models of experiencedependent cortical reorganization.

M Feb. 23 W Feb. 25 
Recurrent networks. Hopfield networks; pattern completion;
models of associative memory; winnertakeall 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. Integrateandfire model; neural
encoding and decoding. 