VS298: Reading: Difference between revisions
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* '''HKP''' Chapter 8 | * '''HKP''' Chapter 8 | ||
* '''PDP''' [http://connes.berkeley.edu/~amir/vs298/chap9.pdf Chapter 9] (full text of Michael Jordan's tutorial on linear algebra, including section on eigenvectors) | * '''PDP''' [http://connes.berkeley.edu/~amir/vs298/chap9.pdf Chapter 9] (full text of Michael Jordan's tutorial on linear algebra, including section on eigenvectors) | ||
==== 25 Sep ==== | |||
* '''HKP''' Chapter 9 | |||
==== 30 Sep ==== | ==== 30 Sep ==== | ||
* Foldiak, P. [http://connes.berkeley.edu/~amir/vs298/foldiak90.pdf Forming sparse representations by local anti-Hebbian learning]. Biol. Cybern. 64, 165-170 (1990). | * Foldiak, P. [http://connes.berkeley.edu/~amir/vs298/foldiak90.pdf Forming sparse representations by local anti-Hebbian learning]. Biol. Cybern. 64, 165-170 (1990). | ||
* Olshausen BA, Field DJ Emergence of simple-cell receptive field properties by learning a sparse code for natural images, Nature, 381: 607-609. (1996) | * Olshausen BA, Field DJ Emergence of simple-cell receptive field properties by learning a sparse code for natural images, Nature, 381: 607-609. (1996) | ||
Revision as of 17:59, 2 October 2008
For each lecture, we also have a list of optional reading corresponding to ideas discussed in lecture. You may read these if you are interested in the particular topic: Optional Reading
2 Sep
- Bell, A.J. Levels and loops: the future of artificial intelligence and neuroscience. Phil Trans: Bio Sci. 354:2013--2020 (1999) here or here
- 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.
04 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:
16 Sep
- Handout on supervised learning in single-stage feedforward networks
18 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.).
- NetTalk demo
23 Sep
- Handout: Hebbian learning and PCA
- HKP Chapter 8
- PDP Chapter 9 (full text of Michael Jordan's tutorial on linear algebra, including section on eigenvectors)
25 Sep
- HKP Chapter 9
30 Sep
- Foldiak, P. Forming sparse representations by local anti-Hebbian learning. Biol. Cybern. 64, 165-170 (1990).
- Olshausen BA, Field DJ Emergence of simple-cell receptive field properties by learning a sparse code for natural images, Nature, 381: 607-609. (1996)
