Bruno: /* Reinforcement learning */

2006-09-27T18:12:36Z

Reinforcement learning

New page

== Syllabus ==

==== Introduction ====
# Theory and modeling in neuroscience
# Descriptive vs. functional models
# Turing vs. neural computation
* '''Reading''': '''HKP''' chapter 1

==== Linear neuron models ====

# Linear systems: vectors, matrices, linear neuron models
# Perceptron model and linear separability
* '''Reading''': '''HKP''' chapter 5, '''DJCM''' chapters 38-40

==== Supervised learning ====

# Perceptron learning rule
# Adaptation in linear neurons, Widrow-Hoff rule
# Objective functions and gradient descent
# Multilayer networks and backpropagation
* '''Reading''': '''HKP''' chapter 6, 7, '''DJCM''' chapters 38-40, 44

==== Reinforcement learning ====

# Classical conditioning and Rescorla-Wagner rule
# Temporal difference learning
# Actor-critic learning
* '''Reading''': '''DA''' chapter 9

==== Unsupervised learning ====

# Linear Hebbian learning and PCA, decorrelation
# Winner-take-all networks and clustering
# Sparse, distributed coding
* '''Reading''': '''HKP''' chapter 8, '''DJCM''' chapter 36, '''DA''' chapter 8, 10

==== Plasticity and cortical maps ====

# Self-organizing maps, Kohonen nets
# Models of experience dependent learning and cortical reorganization
* '''Reading''': '''HKP''' chapter 9, '''DA''' chapter 8

==== Recurrent networks ====

# Hopfield networks
# Pattern completion
# Line attractors and `bump circuits’
# Models of associative memory
* '''Reading''': '''HKP''' chapter 2-3, '''DJCM''' chapter 42, '''DA''' chapter 7

==== Probabilistic models and inference ====

# Probability theory and Bayes’ rule
# Learning and inference in generative models
# The mixture of Gaussians model
# Boltzmann machines
# Sparse coding and ‘ICA’
* '''Reading''': '''DJCM''' chapter 1-3, 20-24,41,43, '''DA''' chapter 10

==== Neural implementations ====

# Integrate-and-fire model
# Neural encoding and decoding
# Limits of precision in neurons
* '''Reading''': '''DA''' chapter 1-4, 5.4

VS298 (Fall 06): Syllabus - Revision history

Bruno: /* Reinforcement learning */