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Difference between revisions of "VS265: Syllabus"

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(Syllabus)
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* Dynamical models
 
* Dynamical models
  
==== Nov. 18,20,25:  Probabilistic models and inference ====
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==== Nov. 18,20,25, Dec. 2:  Probabilistic models and inference ====
  
 
* Probability theory and Bayes’ rule
 
* Probability theory and Bayes’ rule
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* The mixture of Gaussians model
 
* The mixture of Gaussians model
 
* Boltzmann machines
 
* Boltzmann machines
* Sparse coding and ‘ICA’
 
 
* Kalman filter model
 
* Kalman filter model
 
* Energy-based models
 
* Energy-based models
  
==== Dec. 2,4:  Neural implementations ====
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==== Dec. 4:  Guest lecture (Tony Bell) ====
 +
* Sparse coding and ‘ICA’
 +
 
 +
==== Dec. 9:  Neural implementations ====
  
 
* Integrate-and-fire model
 
* Integrate-and-fire model
 
* Neural encoding and decoding
 
* Neural encoding and decoding
 
* Limits of precision in neurons
 
* Limits of precision in neurons
* Neural synchrony and phase-based coding
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<!-- * Neural synchrony and phase-based coding -->
 
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==== Dec. 9,11:  Special topics ====
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* TBD
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* TBD
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Revision as of 07:05, 10 December 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: Ecological utility and the mythical neural code
  • Pentti Kanerva: Computing with 10,000 bits

Oct. 14: Unsupervised learning (continued)

Oct. 16: Guest lecture

  • Tom Dean, Google: Connectomics

Oct. 21,23,28: Sparse, distributed coding

  • Autoencoders
  • Natural image statistics
  • Projection pursuit

Oct. 30, Nov. 4: Plasticity and cortical maps

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

Nov. 6: Manifold learning

  • Local linear embedding, Isomap

Nov. 13: Recurrent networks

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

Nov. 18,20,25, Dec. 2: Probabilistic models and inference

  • Probability theory and Bayes’ rule
  • Learning and inference in generative models
  • The mixture of Gaussians model
  • Boltzmann machines
  • Kalman filter model
  • Energy-based models

Dec. 4: Guest lecture (Tony Bell)

  • Sparse coding and ‘ICA’

Dec. 9: Neural implementations

  • Integrate-and-fire model
  • Neural encoding and decoding
  • Limits of precision in neurons
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