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Difference between revisions of "VS265: Reading"

From RedwoodCenter

(9 Dec)
(9 Dec)
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Spiking neurons:
 
Spiking neurons:
* DA chapter 1-4, 5.4  
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* DA chapters 1-4, 5.4  
 
* 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.
 
* 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.
 
* Chris Eliasmith, Charlie Anderson, [http://books.google.com/books?id=J6jz9s4kbfIC Neural Engineering:  Computation, Representation, and Dynamics in Neurobiological Systems], MIT Press, 2004.  (Chapter 4 will be emailed to the class.)
 
* Chris Eliasmith, Charlie Anderson, [http://books.google.com/books?id=J6jz9s4kbfIC Neural Engineering:  Computation, Representation, and Dynamics in Neurobiological Systems], MIT Press, 2004.  (Chapter 4 will be emailed to the class.)

Revision as of 17:13, 10 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 Dec

9 Dec

Kalman filter:

Spiking neurons:

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