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Difference between revisions of "VS265: Reading"

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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]
 
* 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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==== 2 Dec ====
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* Crick and Mitchison theory on 'unlearning' during sleep - [http://redwood.berkeley.edu/vs265/crick-mitchison-sleep.pdf paper]
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* Application of Boltzmann machines to neural data analysis:<br>
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E. Schneidman, M.J. Berry, R. Segev and W. Bialek,[http://www.nature.com/nature/journal/v440/n7087/full/nature04701.html Weak pairwise correlations imply strongly correlated network states in a neural population], Nature 4400 (7087) (2006), pp. 1007-1012.<br>
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J. Shlens, G.D. Field, J.L. Gauthier, M.I. Grivich, D. Petrusca, A. Sher, A.M. Litke and E.J. Chichilnisky, [http://www.jneurosci.org/cgi/content/abstract/26/32/8254 The structure of multi-neuron firing patterns in primate retina], J Neurosci 260 (32) (2006), pp. 8254-8266.<br>
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Urs paper
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* Robbie Jacobs' [http://www.bcs.rochester.edu/people/robbie/jacobslab/cheat_sheet/sensoryIntegration.pdf notes on Kalman filter]
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* [http://redwood.berkeley.edu/vs265/kalman.m kalman.m] demo script
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* Greg Welch's [http://www.cs.unc.edu/~welch/kalman/kalmanIntro.html  tutorial on Kalman filter]
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* [http://vision.ucla.edu/~doretto/projects/dynamic-textures.html Dynamic texture models]
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* Kevin Murphy's [http://redwood.berkeley.edu/vs265/murphy-hmm.pdf  HMM tutorial]
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==== 21 Nov ====
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* [http://redwood.berkeley.edu/vs265/info-theory.pdf Information theory primer]
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* [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-natgrad.pdf Natural gradients made quick and dirty]
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* Jascha Sohl-Dickstein, [http://redwood.berkeley.edu/vs265/jascha-cookbook.pdf Natural gradient cookbook]
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* Bell & Sejnowski, [http://redwood.berkeley.edu/vs265/tony-ica.pdf An Information-Maximization Approach to Blind Separation and Blind Deconvolution], Neural Comp, 1995.
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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.
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* Hyvarinen, Hoyer, Inki, [http://redwood.berkeley.edu/vs265/TICA.pdf Topographic Independent Component Analysis], Neural Comp, 2001.
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* 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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* Shao & Cottrell paper on [http://redwood.berkeley.edu/vs265/hshan-nips06.pdf Recursive ICA], NIPS 2006.
  
  

Revision as of 17:41, 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

  • Crick and Mitchison theory on 'unlearning' during sleep - paper
  • Application of Boltzmann machines to neural data analysis:

E. Schneidman, M.J. Berry, R. Segev and W. Bialek,Weak pairwise correlations imply strongly correlated network states in a neural population, Nature 4400 (7087) (2006), pp. 1007-1012.
J. Shlens, G.D. Field, J.L. Gauthier, M.I. Grivich, D. Petrusca, A. Sher, A.M. Litke and E.J. Chichilnisky, The structure of multi-neuron firing patterns in primate retina, J Neurosci 260 (32) (2006), pp. 8254-8266.
Urs paper


21 Nov


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