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Difference between revisions of "VS265: Reading"

From RedwoodCenter

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* [http://cs.brown.edu/people/tld/note/blog/14/10/16/index.html Lecture notes and slides]
 
* [http://cs.brown.edu/people/tld/note/blog/14/10/16/index.html Lecture notes and slides]
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==== 21 Oct ====
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* Barlow, HB. [http://redwood.berkeley.edu/vs265/barlow1972.pdf Single units and sensation: A neuron doctrine for perceptual psychology?]  Perception, volume 1, pp. 371 -394 (1972)
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* Foldiak, P. [http://redwood.berkeley.edu/vs265/foldiak90.pdf Forming sparse representations by local anti-Hebbian learning]. Biol. Cybern. 64, 165-170 (1990).
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* Olshausen BA, Field DJ. [http://redwood.berkeley.edu/vs265/bruno-nature.pdf Emergence of simple-cell receptive field properties by learning a sparse code for natural images], Nature, 381: 607-609. (1996)
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Additional readings:
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* Rozell, Johnson, Baraniuk, Olshausen. [http://redwood.berkeley.edu/vs265/rozell-sparse-coding-nc08.pdf Sparse Coding via Thresholding and Local Competition in Neural Circuits], Neural Computation 20, 2526–2563 (2008).
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* Zylberberg, Murphy, DeWeese, [http://redwood.berkeley.edu/vs265/zylberberg-sparse-coding.pdf A sparse coding model with synaptically local plasticity and spiking neurons can account for the diverse shapes of V1 simple cell receptive fields.], PLOS Computational Biology, 7,  (2011).  (sparse coding with spiking neurons)
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* Olshausen [http://redwood.berkeley.edu/bruno/papers/icip03.pdf Sparse coding of time-varying natural images], ICIP 2003. (convolution sparse coding of video)
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* Smith E, Lewicki MS. [http://redwood.berkeley.edu/vs265/smith-lewicki-nature06.pdf Efficient auditory coding], Nature Vol 439 (2006).  (convolution sparse coding of sound)
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==== 23 Oct ====
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* '''HKP''' chapter 9, '''DA''' chapter 8
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* [http://redwood.berkeley.edu/vs265/miller89.pdf Ocular dominance column development: Analysis and simulation] by Miller, Keller and Stryker.
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* [http://redwood.berkeley.edu/vs265/durbin-mitchison.pdf A dimension reduction framework for understanding cortical maps] by R. Durbin and G. Mitchison.
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* [http://redwood.berkeley.edu/vs265/horton05.pdf The cortical column: a structure without a function] by Jonathan C. Horton and Daniel L. Adams
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Optional:
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* Gary Blasdel, [http://redwood.berkeley.edu/vs265/blasdel1992.pdf Orientation selectivity, preference, and continuity in monkey striate cortex.], J Neurosci, 1992.  Another source of many of nice images are in the galleries on Amiram Grinvald's site: [http://www.weizmann.ac.il/brain/grinvald/]
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* From Clay Reid's lab, [http://www.nature.com/nature/journal/v433/n7026/abs/nature03274.html Functional imaging with cellular resolution reveals precise micro-architecture in visual cortex]. Make sure you look at the supplementary material and videos on their web site (seems partly broken) [http://reid.med.harvard.edu/movies.html].
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* '''Reading''': '''HKP''' chapter 9, '''DA''' chapter 8 -->
 
 
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* '''Reading''': '''HKP''' chapter 7 (sec. 7.1),'''DJCM''' chapter 1-3, 20-24,41,43, '''DA''' chapter 10 -->
 
* '''Reading''': '''HKP''' chapter 7 (sec. 7.1),'''DJCM''' chapter 1-3, 20-24,41,43, '''DA''' chapter 10 -->
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<!-- ICA
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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) -->
 
<!-- neural implementations
 
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* '''Reading''': '''DA''' chapter 1-4, 5.4 -->
 
* '''Reading''': '''DA''' chapter 1-4, 5.4 -->

Revision as of 18:17, 21 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)

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

21 Oct

Additional readings:

23 Oct

Optional:



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