VS298: Syllabus - Revision history

Bruno: /* Probabilistic models and inference */

2008-11-04T06:31:45Z

Probabilistic models and inference

← Older revision		Revision as of 06:23, 4 November 2008
Line 48:		Line 48:
	# Probability theory and Bayes’ rule		# Probability theory and Bayes’ rule
	# Learning and inference in generative models		# Learning and inference in generative models
	~~# Kalman filter model~~
	# The mixture of Gaussians model		# The mixture of Gaussians model
	# Boltzmann machines		# Boltzmann machines
	# Sparse coding and ‘ICA’		# Sparse coding and ‘ICA’
			# Kalman filter model
	# Energy-based models		# Energy-based models
	* '''Reading''': '''DJCM''' chapter 1-3, 20-24,41,43, '''DA''' chapter 10		* '''Reading''': '''DJCM''' chapter 1-3, 20-24,41,43, '''DA''' chapter 10

Bruno: /* Plasticity and cortical maps */

2008-10-15T18:17:13Z

Plasticity and cortical maps

← Older revision		Revision as of 18:09, 15 October 2008
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	==== Plasticity and cortical maps ====		==== Plasticity and cortical maps ====

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

Bruno: /* Unsupervised learning */

2008-10-15T18:16:16Z

Unsupervised learning

← Older revision		Revision as of 18:08, 15 October 2008
Line 26:		Line 26:
	# Winner-take-all networks and clustering		# Winner-take-all networks and clustering
	# Sparse, distributed coding		# Sparse, distributed coding
	'''Reading''': '''HKP''' chapter 8, '''DJCM''' chapter 36, '''DA''' chapter 8, 10		* '''Reading''': '''HKP''' chapter 8, '''DJCM''' chapter 36, '''DA''' chapter 8, 10

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

Bruno: /* Unsupervised learning */

2008-10-15T18:15:50Z

Unsupervised learning

← Older revision		Revision as of 18:07, 15 October 2008
Line 26:		Line 26:
	# Winner-take-all networks and clustering		# Winner-take-all networks and clustering
	# Sparse, distributed coding		# Sparse, distributed coding
	~~# Manifold learning~~		'''Reading''': '''HKP''' chapter 8, '''DJCM''' chapter 36, '''DA''' chapter 8, 10
	* '''Reading''': '''HKP''' chapter 8, '''DJCM''' chapter 36, '''DA''' chapter 8, 10

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

Bruno: /* Supervised learning */

2008-09-17T17:47:42Z

Supervised learning

← Older revision		Revision as of 17:39, 17 September 2008
Line 19:		Line 19:
	# Objective functions and gradient descent		# Objective functions and gradient descent
	# Multilayer networks and backpropagation		# Multilayer networks and backpropagation
	* '''Reading''': '''HKP''' chapter 6, 7, '''DJCM''' chapters 38-40, 44, '''DA''' chapter 8 (~~sections~~ 4-6)		* '''Reading''': '''HKP''' chapter 6, 7, '''DJCM''' chapters 38-40, 44, '''DA''' chapter 8 (sec. 4-6)

	==== Unsupervised learning ====		==== Unsupervised learning ====

Bruno: /* Supervised learning */

2008-09-17T17:47:28Z

Supervised learning

← Older revision		Revision as of 17:39, 17 September 2008
Line 19:		Line 19:
	# Objective functions and gradient descent		# Objective functions and gradient descent
	# Multilayer networks and backpropagation		# Multilayer networks and backpropagation
	* '''Reading''': '''HKP''' chapter 6, 7, '''DJCM''' chapters 38-40, 44		* '''Reading''': '''HKP''' chapter 6, 7, '''DJCM''' chapters 38-40, 44, '''DA''' chapter 8 (sections 4-6)

	==== Unsupervised learning ====		==== Unsupervised learning ====

Bruno: /* Syllabus */

2008-08-26T22:14:20Z

Syllabus

← Older revision		Revision as of 22:06, 26 August 2008
Line 7:		Line 7:
	* '''Reading''': '''HKP''' chapter 1		* '''Reading''': '''HKP''' chapter 1

	==== ~~Linear neuron~~ models ====		==== Neuron models ====

	# Linear systems: vectors, matrices, linear neuron models		# Linear systems: vectors, matrices, linear neuron models
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	# Multilayer networks and backpropagation		# Multilayer networks and backpropagation
	* '''Reading''': '''HKP''' chapter 6, 7, '''DJCM''' chapters 38-40, 44		* '''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 ====		==== Unsupervised learning ====
Line 33:		Line 26:
	# Winner-take-all networks and clustering		# Winner-take-all networks and clustering
	# Sparse, distributed coding		# Sparse, distributed coding
			# Manifold learning
	* '''Reading''': '''HKP''' chapter 8, '''DJCM''' chapter 36, '''DA''' chapter 8, 10		* '''Reading''': '''HKP''' chapter 8, '''DJCM''' chapter 36, '''DA''' chapter 8, 10

Line 38:		Line 32:

	# Self-organizing maps, Kohonen nets		# Self-organizing maps, Kohonen nets
			# Cortical maps
	# Models of experience dependent learning and cortical reorganization		# Models of experience dependent learning and cortical reorganization
	* '''Reading''': '''HKP''' chapter 9, '''DA''' chapter 8		* '''Reading''': '''HKP''' chapter 9, '''DA''' chapter 8
Line 53:		Line 48:
	# Probability theory and Bayes’ rule		# Probability theory and Bayes’ rule
	# Learning and inference in generative models		# Learning and inference in generative models
			# Kalman filter model
	# The mixture of Gaussians model		# The mixture of Gaussians model
	# Boltzmann machines		# Boltzmann machines
	# Sparse coding and ‘ICA’		# Sparse coding and ‘ICA’
			# Energy-based models
	* '''Reading''': '''DJCM''' chapter 1-3, 20-24,41,43, '''DA''' chapter 10		* '''Reading''': '''DJCM''' chapter 1-3, 20-24,41,43, '''DA''' chapter 10

Line 63:		Line 60:
	# Neural encoding and decoding		# Neural encoding and decoding
	# Limits of precision in neurons		# Limits of precision in neurons
			# Neural synchrony and phase-based coding
	* '''Reading''': '''DA''' chapter 1-4, 5.4		* '''Reading''': '''DA''' chapter 1-4, 5.4

Bruno at 22:07, 26 August 2008

2008-08-26T22:07:54Z

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