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Difference between revisions of "VS265: Reading"

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* [http://www.aiai.ed.ac.uk/events/lighthill1973/1973-BBC-Lighthill-Controversy.mov 1973 Lighthill debate on future of AI]
 
* [http://www.aiai.ed.ac.uk/events/lighthill1973/1973-BBC-Lighthill-Controversy.mov 1973 Lighthill debate on future of AI]
 
Optional:
 
Optional:
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* Land, MF and Fernald, RD. [http://redwood.berkeley.edu/vs265/landfernald92.pdf The Evolution of Eyes], Ann Revs Neuro, 1992.
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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.
 
* 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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* '''HKP''' chapter 5, '''DJCM''' chapters 38-40
 
* '''HKP''' chapter 5, '''DJCM''' chapters 38-40
 
* Mead, C. [http://redwood.berkeley.edu/vs265/Mead-intro.pdf Chapter 1: Introduction] and [http://redwood.berkeley.edu/vs265/Mead-neurons.pdf Chapter 4: Neurons] from ''Analog VLSI and Neural Systems'', Addison-Wesley, 1989.
 
* Mead, C. [http://redwood.berkeley.edu/vs265/Mead-intro.pdf Chapter 1: Introduction] and [http://redwood.berkeley.edu/vs265/Mead-neurons.pdf Chapter 4: Neurons] from ''Analog VLSI and Neural Systems'', Addison-Wesley, 1989.
* [http://redwood.berkeley.edu/vs265/linear-neuron/linear-neuron-models.html Linear neuron models]
 
 
* [http://redwood.berkeley.edu/vs265/lti-conv/lti-convolution.html Linear time-invariant systems and convolution]
 
* [http://redwood.berkeley.edu/vs265/lti-conv/lti-convolution.html Linear time-invariant systems and convolution]
 
* [http://redwood.berkeley.edu/vs265/diffeq-sim/diffeq-sim.html Simulating differential equations]
 
* [http://redwood.berkeley.edu/vs265/diffeq-sim/diffeq-sim.html Simulating differential equations]
 
* [http://redwood.berkeley.edu/vs265/dynamics/dynamics.html Dynamics]
 
* [http://redwood.berkeley.edu/vs265/dynamics/dynamics.html Dynamics]
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* Carandini M, Heeger D (1994) [http://redwood.berkeley.edu/vs265/carandini-heeger.pdf Summation and division by neurons in primate visual cortex.]  Science, 264: 1333-1336.
 
* Carandini M, Heeger D (1994) [http://redwood.berkeley.edu/vs265/carandini-heeger.pdf Summation and division by neurons in primate visual cortex.]  Science, 264: 1333-1336.
 
Optional:
 
* Land, MF and Fernald, RD. [http://redwood.berkeley.edu/vs265/landfernald92.pdf The Evolution of Eyes], Ann Revs Neuro, 1992.
 
* 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.
 
  
 
==== 4 Sept ====
 
==== 4 Sept ====
* '''Reading''': '''HKP''' chapter 6, '''DJCM''' chapters 38-40, 44, '''DA''' chapter 8 (sec. 4-6)
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* '''HKP''' chapter 6, '''DJCM''' chapters 38-40, 44, '''DA''' chapter 8 (sec. 4-6)
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* Jordan, M.I. [http://redwood.berkeley.edu/vs265/PDP.pdf An Introduction to Linear Algebra in Parallel Distributed Processing] in McClelland and Rumelhart, ''Parallel Distributed Processing'', MIT Press, 1985.
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* [http://redwood.berkeley.edu/vs265/linear-neuron/linear-neuron-models.html Linear neuron models]
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* [http://redwood.berkeley.edu/vs265/linear-algebra/linear-algebra.html Linear algebra primer]
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* [http://redwood.berkeley.edu/vs265/superlearn_handout1.pdf Handout] on supervised learning in single-stage feedforward networks
  
 
<!-- unsupervised learning:   
 
<!-- unsupervised learning:   
 
* '''Reading''': '''HKP''' chapter 8, '''DJCM''' chapter 36, '''DA''' chapter 8, 10 -->
 
* '''Reading''': '''HKP''' chapter 8, '''DJCM''' chapter 36, '''DA''' chapter 8, 10 -->
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<!-- plasticity and cortical maps
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* '''Reading''': '''HKP''' chapter 9, '''DA''' chapter 8 -->
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<!-- recurrent networks
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* '''Reading''': '''HKP''' chapters 2, 3 (sec. 3.3-3.5), 7 (sec. 7.2-7.3), '''DJCM''' chapter 42, '''DA''' chapter 7 -->
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<!-- probabilistic models and inference
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* '''Reading''': '''HKP''' chapter 7 (sec. 7.1),'''DJCM''' chapter 1-3, 20-24,41,43, '''DA''' chapter 10 -->
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<!-- neural implementations
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* '''Reading''': '''DA''' chapter 1-4, 5.4 -->

Revision as of 04:37, 1 September 2014

28 Aug

Optional:

2 Sept

Optional

4 Sept




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