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Difference between revisions of "VS265: Reading"

From RedwoodCenter

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* Jordan, M.I. [http://redwood.berkeley.edu/vs265/PDP.pdf An Introduction to Linear Algebra in Parallel Distributed Processing] in McClelland and Rumelhart, ''Parallel Distributed Processing'', MIT Press, 1985.
 
* Jordan, M.I. [http://redwood.berkeley.edu/vs265/PDP.pdf An Introduction to Linear Algebra in Parallel Distributed Processing] in McClelland and Rumelhart, ''Parallel Distributed Processing'', MIT Press, 1985.
  
==== Sept 11:  Multicompartment models, dendritic integration ====
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==== Sept 11:  Multicompartment models, dendritic integration (Rhodes guest lecture) ====
 
* Koch, Single Neuron Computation, Chapter 19 [https://www.dropbox.com/s/rb24w33fqar7gjp/koch_ch19_small.pdf?dl=0 pdf]
 
* Koch, Single Neuron Computation, Chapter 19 [https://www.dropbox.com/s/rb24w33fqar7gjp/koch_ch19_small.pdf?dl=0 pdf]
 
* Rhodes P (1999) [http://redwood.berkeley.edu/vs265/Rhodes-review.pdf Functional Implications of Active Currents in the Dendrites of Pyramidal Neurons]
 
* Rhodes P (1999) [http://redwood.berkeley.edu/vs265/Rhodes-review.pdf Functional Implications of Active Currents in the Dendrites of Pyramidal Neurons]
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* Dan, Atick, Reid. [http://www.jneurosci.org/cgi/reprint/16/10/3351.pdf Efficient Coding of Natural Scenes in the Lateral Geniculate Nucleus: Experimental Test of a Computational Theory], J Neuroscience, 1996.
 
* Dan, Atick, Reid. [http://www.jneurosci.org/cgi/reprint/16/10/3351.pdf Efficient Coding of Natural Scenes in the Lateral Geniculate Nucleus: Experimental Test of a Computational Theory], J Neuroscience, 1996.
  
====Sept 30, Oct 2: Attractor Networks and Associative Memories ====
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==== Sept 30, Oct 2: Attractor Networks and Associative Memories (Sommer guest lectures) ====
 
* "HKP" Chapter 2 and 3 (sec. 3.3-3.5), 7 (sec. 7.2-7.3), '''DJCM''' chapter 42, '''DA''' chapter 7
 
* "HKP" Chapter 2 and 3 (sec. 3.3-3.5), 7 (sec. 7.2-7.3), '''DJCM''' chapter 42, '''DA''' chapter 7
 
* [http://redwood.berkeley.edu/vs265/attractor-networks.pdf Handout] on attractor networks - their learning, dynamics and how they differ from feed-forward networks
 
* [http://redwood.berkeley.edu/vs265/attractor-networks.pdf Handout] on attractor networks - their learning, dynamics and how they differ from feed-forward networks
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* [https://www.dropbox.com/s/mh40nnj5d53o183/222960a0.pdf?dl=0 Willshaw69]
 
* [https://www.dropbox.com/s/mh40nnj5d53o183/222960a0.pdf?dl=0 Willshaw69]
  
====Oct 7: Ecological utility  and the mythical neural code ====
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==== Oct 7: Ecological utility  and the mythical neural code (Feldman guest lecture) ====
 
* [ftp://ftp.icsi.berkeley.edu/pub/feldman/eeu.pdf Feldman10] Ecological utility and the mythical neural code
 
* [ftp://ftp.icsi.berkeley.edu/pub/feldman/eeu.pdf Feldman10] Ecological utility and the mythical neural code
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==== Oct 9: Hyperdimensional computing (Kanerva guest lecture) ====
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* Kanerva, [http://redwood.berkeley.edu/vs265/Kanerva-allerton2014.pdf Computing with 10,000-bit words]
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* Kanerva, [http://redwood.berkeley.edu/vs265/kanerva09-hyperdimensional.pdf Hyperdimensional Computing]
  
  

Revision as of 21:43, 9 October 2014

Aug 28: Introduction

Optional:

Sept 2: Neuron models

Background reading on dynamics, linear time-invariant systems and convolution, and differential equations:

Sept 4: Linear neuron, Perceptron

Background on linear algebra:

Sept 11: Multicompartment models, dendritic integration (Rhodes guest lecture)

Sept. 16, 18: Supervised learning

  • HKP Chapters 5, 6
  • Handout on supervised learning in single-stage feedforward networks
  • Handout on supervised learning in multi-layer feedforward networks - "back propagation"

Further reading:

Sept. 23, 24: Unsupervised learning

  • HKP Chapters 8 and 9, DJCM chapter 36, DA chapter 8, 10
  • Handout: Hebbian learning and PCA
  • PDP Chapter 9 (full text of Michael Jordan's tutorial on linear algebra, including section on eigenvectors)

Optional:

Sept 30, Oct 2: Attractor Networks and Associative Memories (Sommer guest lectures)

  • "HKP" Chapter 2 and 3 (sec. 3.3-3.5), 7 (sec. 7.2-7.3), DJCM chapter 42, DA chapter 7
  • Handout on attractor networks - their learning, dynamics and how they differ from feed-forward networks
  • Hopfield82
  • Hopfield84
  • Willshaw69

Oct 7: Ecological utility and the mythical neural code (Feldman guest lecture)

  • Feldman10 Ecological utility and the mythical neural code

Oct 9: Hyperdimensional computing (Kanerva guest lecture)


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