VS265: Syllabus: Difference between revisions

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==== Sept. 9,11: Guest lectures ====
==== Sept. 9,11: Guest lectures ====


* TBD
* 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
* 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
 
==== Oct. 16: Guest lecture ====
 
* Tom Dean, Google:  Connectomics
 
==== Oct. 21:  Sparse, distributed coding ====


* Autoencoders
* Autoencoders
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* Projection pursuit
* Projection pursuit


==== Oct. 7:  Plasticity and cortical maps ====
==== 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 ====
==== Oct. 28:  Manifold learning ====
 
* TBD
 
==== Oct. 14:  Manifold learning ====


* Local linear embedding, Isomap
* Local linear embedding, Isomap


==== Oct. 16Guest lecture ====
==== Oct. 30, Nov. 4,6Recurrent networks ====
 
* Tom Dean, Google:  Connectomics


==== Oct. 21,23,28,30:  Recurrent networks ====
* Hopfield networks, memories as 'basis of attraction'
* Hopfield networks
* Models of associative memory, pattern completion
* 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 ====
==== 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 ====
==== 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