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Difference between revisions of "VS265: Reading"

From RedwoodCenter

(2 Dec)
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** Kevin Murphy's [http://redwood.berkeley.edu/vs265/murphy-hmm.pdf  HMM tutorial]
 
** Kevin Murphy's [http://redwood.berkeley.edu/vs265/murphy-hmm.pdf  HMM tutorial]
  
==== 21 Nov ====
+
==== 4 Nov ====
  
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* DA Chapter 10
 
* [http://redwood.berkeley.edu/vs265/info-theory.pdf Information theory primer]  
 
* [http://redwood.berkeley.edu/vs265/info-theory.pdf Information theory primer]  
 
* [http://redwood.berkeley.edu/vs265/handout-sparse-08.pdf Sparse coding and ICA handout]
 
* [http://redwood.berkeley.edu/vs265/handout-sparse-08.pdf Sparse coding and ICA handout]
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* Jascha Sohl-Dickstein, [http://redwood.berkeley.edu/vs265/jascha-cookbook.pdf Natural gradient cookbook]
 
* Jascha Sohl-Dickstein, [http://redwood.berkeley.edu/vs265/jascha-cookbook.pdf Natural gradient cookbook]
 
* Bell & Sejnowski, [http://redwood.berkeley.edu/vs265/tony-ica.pdf An Information-Maximization Approach to Blind Separation and Blind Deconvolution], Neural Comp, 1995.
 
* Bell & Sejnowski, [http://redwood.berkeley.edu/vs265/tony-ica.pdf An Information-Maximization Approach to Blind Separation and Blind Deconvolution], Neural Comp, 1995.
* Karklin & Simoncelli, [[http://redwood.berkeley.edu/vs265/karklin-simoncelli.pdf Efficient coding of natural images with a population of noisy Linear-Nonlinear neurons], NIPS 2011.
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* Simoncelli, Olshausen. [http://redwood.berkeley.edu/vs265/simoncelli01-reprint.pdf Natural Image Statistics and Neural Representation], Annu. Rev. Neurosci. 2001. 24:1193–216.
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* van Hateren & Ruderman [http://redwood.berkeley.edu/vs265/vanhateren-ruderman98.pdf Independent component analysis of natural image sequences], Proc. R. Soc. Lond. B (1998) 265. (blocked sparse coding/ICA of video)
 
* Hyvarinen, Hoyer, Inki, [http://redwood.berkeley.edu/vs265/TICA.pdf Topographic Independent Component Analysis], Neural Comp, 2001.
 
* Hyvarinen, Hoyer, Inki, [http://redwood.berkeley.edu/vs265/TICA.pdf Topographic Independent Component Analysis], Neural Comp, 2001.
 
* Karklin & Lewicki paper on  [http://redwood.berkeley.edu/vs265/karklin-lewicki2003.pdf Learning Higher-Order Structure in Natural Images], Network 2003.
 
* Karklin & Lewicki paper on  [http://redwood.berkeley.edu/vs265/karklin-lewicki2003.pdf Learning Higher-Order Structure in Natural Images], Network 2003.
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* Simoncelli, Olshausen. [http://redwood.berkeley.edu/vs265/simoncelli01-reprint.pdf Natural Image Statistics and Neural Representation], Annu. Rev. Neurosci. 2001. 24:1193–216.
+
* van Hateren & Ruderman [http://redwood.berkeley.edu/vs265/vanhateren-ruderman98.pdf Independent component analysis of natural image sequences], Proc. R. Soc. Lond. B (1998) 265. (blocked sparse coding/ICA of video) -->
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<!-- neural implementations
 
<!-- neural implementations
* '''Reading''': '''DA''' chapter 1-4, 5.4 -->
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* '''Reading''': '''DA''' chapter 1-4, 5.4  
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* Karklin & Simoncelli, [[http://redwood.berkeley.edu/vs265/karklin-simoncelli.pdf Efficient coding of natural images with a population of noisy Linear-Nonlinear neurons], NIPS 2011.
 +
-->

Revision as of 18:07, 2 December 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)

Oct 16: Structural and Functional Connectomics (Tom Dean guest lecture)

21,23,28 Oct

Additional readings:

30 Oct, 4 Nov

Optional:

Re-organization in response to cortical lesions:

6 Nov

Additional reading:

6, 13 Nov

13,18 Nov

20,25 Nov

2 Dec

4 Nov



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