|W Jan. 7
is a model? What makes a good model? Models in neuroscience.
|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
|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
|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.