| 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. |