HOME MISSION AND RESEARCH PUBLICATIONS HISTORY PEOPLE SEMINARS COURSES VIDEO ARCHIVE CONTACT

Difference between revisions of "VS265: Reading"

From RedwoodCenter

Line 39: Line 39:
 
* [http://cnl.salk.edu/Research/ParallelNetsPronounce/ NetTalk demo]
 
* [http://cnl.salk.edu/Research/ParallelNetsPronounce/ NetTalk demo]
  
 +
==== Sept. 23, 24: Unsupervised learning ====
 +
* '''HKP''' Chapters 8 and 9, '''DJCM''' chapter 36, '''DA''' chapter 8, 10
 +
* Handout: [http://redwood.berkeley.edu/vs265/hebb-pca-handout.pdf Hebbian learning and PCA]
 +
* '''PDP''' [http://redwood.berkeley.edu/vs265/chap9.pdf Chapter 9] (full text of Michael Jordan's tutorial on linear algebra, including section on eigenvectors)
  
 +
Optional:
 +
* Atick, Redlich. [http://redwood.berkeley.edu/vs265/Atick-Redlich-NC92.pdf What does the retina know about natural scenes?], Neural Computation, 1992.
 +
* Dan, Atick, Reid. [http://www.jneurosci.org/cgi/reprint/16/10/3351.pdf Efficient Coding of Natural Scenes in the Lateral Geniculate Nucleus: Experimental Test of a Computational Theory], J Neuroscience, 1996.
  
<!-- unsupervised learning: 
 
* '''Reading''': '''HKP''' chapter 8, '''DJCM''' chapter 36, '''DA''' chapter 8, 10 -->
 
  
 
<!-- plasticity and cortical maps
 
<!-- plasticity and cortical maps

Revision as of 21:50, 25 September 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

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:




Personal tools