VS298 (Fall 06): Reading: Difference between revisions
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* Pouget, A., and Sejnowski, T.J. (1997) [http://redwood.berkeley.edu/~amir/vs298/pouget-bf-jcn97.pdf Spatial transformations in the parietal cortex using basis functions.] Journal of Cognitive Neuroscience. 9(2):222-237. | * Pouget, A., and Sejnowski, T.J. (1997) [http://redwood.berkeley.edu/~amir/vs298/pouget-bf-jcn97.pdf Spatial transformations in the parietal cortex using basis functions.] Journal of Cognitive Neuroscience. 9(2):222-237. | ||
* [http://www.cnl.salk.edu/ParallelNetsPronounce/index.php NetTalk demo] | * [http://www.cnl.salk.edu/ParallelNetsPronounce/index.php NetTalk demo] | ||
==== 11 Oct ==== | |||
* Handout: [http://redwood.berkeley.edu/~amir/vs298/hebb-pca.pdf Hebbian learning and PCA] | |||
* '''HKP''' Chapter 8 | |||
==== 13 Oct ==== | |||
* Foldiak, P. [http://redwood.berkeley.edu/~amir/vs298/foldiak90.pdf Forming sparse representations by local anti-Hebbian learning]. Biol. Cybern. 64, 165-170 (1990). | |||
Revision as of 21:54, 14 October 2006
29 Aug
- Bell, A.J. Levels and loops: the future of artificial intelligence and neuroscience. Phil Trans: Bio Sci. 354:2013--2020 (1999) here or here
06 Sep
- Dreyfus, H.L. and Dreyfus, S.E. Making a Mind vs. Modeling the Brain: Artificial Intelligence Back at a Branchpoint. Daedalus, Winter 1988.
- Mead, C. Chapter 1: Introduction and Chapter 4: Neurons from Analog VLSI and Neural Systems, Addison-Wesley, 1989.
- Jordan, M.I. An Introduction to Linear Algebra in Parallel Distributed Processing in McClelland and Rumelhart, Parallel Distributed Processing, MIT Press, 1985.
- Zhang K, Sejnowski TJ (2000) A universal scaling law between gray matter and white matter of cerebral cortex. PNAS, 97: 5621–5626.
08 Sep
- Linear neuron models
- Linear time-invariant systems and convolution
- Simulating differential equations
- Carandini M, Heeger D (1994) Summation and division by neurons in primate visual cortex. Science, 264: 1333-1336.
Optional reading for more background:
20 Sep
- Handout on supervised learning in single-stage feedforward networks
22 Sep
- Handout on supervised learning in multi-layer feedforward networks - "backpropagation"
- Y. LeCun, L. Bottou, G. Orr, and K. Muller (1998) "Efficient BackProp," in Neural Networks: Tricks of the trade, (G. Orr and Muller K., eds.).
- Pouget, A., and Sejnowski, T.J. (1997) Spatial transformations in the parietal cortex using basis functions. Journal of Cognitive Neuroscience. 9(2):222-237.
- NetTalk demo
11 Oct
- Handout: Hebbian learning and PCA
- HKP Chapter 8
13 Oct
- Foldiak, P. Forming sparse representations by local anti-Hebbian learning. Biol. Cybern. 64, 165-170 (1990).
