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Difference between revisions of "VS265: Reading"

From RedwoodCenter

(13,18 Nov)
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* [http://www.cs.toronto.edu/~graves/phd.pdf Alex Graves' thesis], see Chapter 4
 
* [http://www.cs.toronto.edu/~graves/phd.pdf Alex Graves' thesis], see Chapter 4
 
* [http://redwood.berkeley.edu/vs265/sussillo-dynamical-systems-curropin.pdf Sussillo, dynamical systems in neuroscience]
 
* [http://redwood.berkeley.edu/vs265/sussillo-dynamical-systems-curropin.pdf Sussillo, dynamical systems in neuroscience]
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==== 20,25 Nov ====
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* HKP chapter 7 (sec. 7.1),DJCM chapter 1-3, 20-24,41,43, DA chapter 10
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* [https://www.dropbox.com/s/naezga6niejfw1n/chapter_preprint.pdf?dl=0 Olshausen (2014) Perception as an Inference Problem]
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* [http://redwood.berkeley.edu/vs265/probability.pdf A probability primer]
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* [http://redwood.berkeley.edu/vs265/bayes-prob.pdf Bayesian probability theory and generative models]
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* [http://redwood.berkeley.edu/vs265/mog.pdf Mixture of Gaussians model ]
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* T.J. Loredo, [http://redwood.berkeley.edu/vs265/loredo-laplace-supernova.pdf From Laplace to supernova SN1987A:  Bayesian inference in astrophysics]
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* '''Reading''': '''HKP''' chapter 7 (sec. 7.1),'''DJCM''' chapter 1-3, 20-24,41,43, '''DA''' chapter 10 -->
 
 
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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.
 
* Simoncelli, Olshausen. [http://redwood.berkeley.edu/vs265/simoncelli01-reprint.pdf Natural Image Statistics and Neural Representation], Annu. Rev. Neurosci. 2001. 24:1193–216.

Revision as of 23:16, 20 November 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



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