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Difference between revisions of "VS265: Syllabus"

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(Sept. 30: Guest lecture)
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==== Sept. 9,11: Guest lectures ====
 
==== Sept. 9,11: Guest lectures ====
  
* TBD
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* Matlab/Python tutorial
 
* Paul Rhodes, Evolved Machines:  Multi-compartment models; dendritic integration
 
* Paul Rhodes, Evolved Machines:  Multi-compartment models; dendritic integration
  
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==== Sept. 30, Oct. 2:  Guest lecture ====
 
==== Sept. 30, Oct. 2:  Guest lecture ====
  
* Fritz Sommer, Associative memories and attractor neural networks
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* Fritz Sommer: Associative memories and attractor neural networks
  
==== Oct. 2:  Sparse, distributed coding ====
+
==== Oct. 7,9: Guest lectures ====
 +
 
 +
* Jerry Feldman:
 +
* Pentti Kanerva: Computing with 10,000 bits
 +
 
 +
==== Oct. 14: Unsupervised learning (continued) ====
 +
 
 +
* Linear Hebbian learning and PCA, decorrelation
 +
* Winner-take-all networks and clustering
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 +
==== Oct. 16: Guest lecture ====
 +
 
 +
* Tom Dean, Google:  Connectomics
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 +
==== Oct. 21:  Sparse, distributed coding ====
  
 
* Autoencoders
 
* Autoencoders
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* Projection pursuit
 
* Projection pursuit
  
==== Oct. 7:  Plasticity and cortical maps ====
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==== Oct. 23:  Plasticity and cortical maps ====
  
 
* Cortical maps
 
* Cortical maps
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* Models of experience dependent learning and cortical reorganization
 
* Models of experience dependent learning and cortical reorganization
  
==== Oct. 9:  Guest lecture ====
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==== Oct. 28:  Manifold learning ====
 
+
* TBD
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==== Oct. 14:  Manifold learning ====
+
  
 
* Local linear embedding, Isomap
 
* Local linear embedding, Isomap
  
==== Oct. 16Guest lecture ====
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==== Oct. 30, Nov. 4,6Recurrent networks ====
 
+
* Tom Dean, Google:  Connectomics
+
  
==== Oct. 21,23,28,30:  Recurrent networks ====
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* Hopfield networks, memories as 'basis of attraction'
* Hopfield networks
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* Models of associative memory, pattern completion
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* Line attractors and `bump circuits’
 
* Line attractors and `bump circuits’
 
* Dynamical models
 
* Dynamical models
  
==== Nov. 4,6,13,18,20,25:  Probabilistic models and inference ====
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==== Nov. 13,18,20,25:  Probabilistic models and inference ====
  
 
* Probability theory and Bayes’ rule
 
* Probability theory and Bayes’ rule
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* Neural synchrony and phase-based coding
 
* Neural synchrony and phase-based coding
  
==== Dec. 9,11:  Guest lectures ====
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==== Dec. 9,11:  Special topics ====
  
 
* TBD
 
* TBD
 
* TBD
 
* TBD

Revision as of 19:07, 14 October 2014

Syllabus

Aug. 28: Introduction

  • Theory and modeling in neuroscience
  • Goals of AI/machine learning vs. theoretical neuroscience
  • Turing vs. neural computation

Sept. 2,4: Neuron models

  • Membrane equation, compartmental model of a neuron
  • Linear systems: vectors, matrices, linear neuron models
  • Perceptron model and linear separability

Sept. 9,11: Guest lectures

  • Matlab/Python tutorial
  • Paul Rhodes, Evolved Machines: Multi-compartment models; dendritic integration

Sept. 16,18: Supervised learning

  • Perceptron learning rule
  • Adaptation in linear neurons, Widrow-Hoff rule
  • Objective functions and gradient descent
  • Multilayer networks and backpropagation

Sept. 23,25: Unsupervised learning

  • Linear Hebbian learning and PCA, decorrelation
  • Winner-take-all networks and clustering

Sept. 30, Oct. 2: Guest lecture

  • Fritz Sommer: Associative memories and attractor neural networks

Oct. 7,9: Guest lectures

  • Jerry Feldman:
  • Pentti Kanerva: Computing with 10,000 bits

Oct. 14: Unsupervised learning (continued)

  • Linear Hebbian learning and PCA, decorrelation
  • Winner-take-all networks and clustering

Oct. 16: Guest lecture

  • Tom Dean, Google: Connectomics

Oct. 21: Sparse, distributed coding

  • Autoencoders
  • Natural image statistics
  • Projection pursuit

Oct. 23: Plasticity and cortical maps

  • Cortical maps
  • Self-organizing maps, Kohonen nets
  • Models of experience dependent learning and cortical reorganization

Oct. 28: Manifold learning

  • Local linear embedding, Isomap

Oct. 30, Nov. 4,6: Recurrent networks

  • Hopfield networks, memories as 'basis of attraction'
  • Line attractors and `bump circuits’
  • Dynamical models

Nov. 13,18,20,25: 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’
  • Kalman filter model
  • Energy-based models

Dec. 2,4: Neural implementations

  • Integrate-and-fire model
  • Neural encoding and decoding
  • Limits of precision in neurons
  • Neural synchrony and phase-based coding

Dec. 9,11: Special topics

  • TBD
  • TBD
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