VS298 (Fall 06): Schedule

Schedule

• Week 1 (Sept. 6,8): Introduction; theory and modeling in neuroscience; linear neuron models; perceptron

• Week 2 (Sept. 13,15): guest lecture (Bell, Wednesday only)

• Week 3 (Sept. 20, 22): Perceptron learning rule; Widrow-Hoff rule; Objective functions and gradient descent; Multilayer networks and backpropagation

• Week 4 (Sept. 27, 29): Reinforcement learning; Theory of associative reward-penalty; Models and critics

• Week 5 (Oct. 4, 6): guest lecture (Russell, Wednesday only)

• Week 6 (Oct. 11, 13): Unsupervised learning; Hebbian learning and PCA; winner-take-all networks and clustering; sparse, distributed coding

• Week 7 (Oct. 18, 20): Plasticity and cortical maps; Self-organizing maps; Kohonen nets; Models of experience dependent learning and cortical reorganization

• Week 8 (Oct. 25, 27): Recurrent networks; attractor dynamics; Hopfield networks, Pattern completion; Line attractors and `bump circuits’

• Week 9 (Nov. 1, 3): Associative memory models

• Week 10 (Nov. 8, 10): Probabilistic models and inference; The mixture of Gaussians model

• Week 11 (Nov. 15, 17): Boltzmann machines

• Week 12 (Nov. 22, 24): Sparse coding and ICA

• Week 13 (Nov. 29, Dec. 1): Neural implementations; Integrate-and-fire model; Neural encoding and decoding; Limits of precision in neurons

• Week 14 (Dec. 6, 8): Special topics; student projects