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Difference between revisions of "VS265: Reading"

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(Sept 4: Linear neuron, Perceptron)
(Aug 28: Introduction)
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* Zhang K, Sejnowski TJ (2000)  [http://redwood.berkeley.edu/vs265/zhang-sejnowski.pdf A universal scaling law between gray matter and white matter of cerebral cortex.]  PNAS, 97: 5621–5626.
 
* Zhang K, Sejnowski TJ (2000)  [http://redwood.berkeley.edu/vs265/zhang-sejnowski.pdf A universal scaling law between gray matter and white matter of cerebral cortex.]  PNAS, 97: 5621–5626.
 
* O'Rourke, N.A et al. [http://redwood.berkeley.edu/vs265/smith-synaptic-diversity.pdf "Deep molecular diversity of mammalian synapses:  why it matters and how to measure it."]  Nature Reviews Neurosci. 13, (2012)
 
* O'Rourke, N.A et al. [http://redwood.berkeley.edu/vs265/smith-synaptic-diversity.pdf "Deep molecular diversity of mammalian synapses:  why it matters and how to measure it."]  Nature Reviews Neurosci. 13, (2012)
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* Stephen Smith [http://smithlab.stanford.edu/Smithlab/AT_Movies.html Array Tomography movies]
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* Solari & Stoner, [http://redwood.berkeley.edu/vs265/solari-stoner-cognitive-consilience.pdf Cognitive Consilience]
  
 
==== Sept 2:  Neuron models ====
 
==== Sept 2:  Neuron models ====

Revision as of 03:40, 5 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

  • HKP chapter 5, DJCM chapters 38-40, 44, DA chapter 8 (sec. 4-6)
  • Linear neuron models
  • Handout on supervised learning in single-stage feedforward networks

Background on linear algebra:




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