# Difference between revisions of "VS298 (Fall 06): Reading"

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* [http://redwood.berkeley.edu/~amir/vs298/probability.pdf A probability primer] | * [http://redwood.berkeley.edu/~amir/vs298/probability.pdf A probability primer] | ||

* [http://redwood.berkeley.edu/~amir/vs298/bayes-prob.pdf Bayesian probability theory and generative models] | * [http://redwood.berkeley.edu/~amir/vs298/bayes-prob.pdf Bayesian probability theory and generative models] | ||

+ | |||

+ | ==== 15 Nov ==== | ||

+ | |||

* [http://redwood.berkeley.edu/~amir/vs298/mog.pdf Mixture of Gaussians model ] | * [http://redwood.berkeley.edu/~amir/vs298/mog.pdf Mixture of Gaussians model ] | ||

+ | * Dayan & Abbott Chapter 10 |

## Revision as of 07:05, 17 November 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

#### 27 Sep

**D&A**Chapter 8

#### 11 Oct

- 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)

#### 13 Oct

- Foldiak, P. Forming sparse representations by local anti-Hebbian learning. Biol. Cybern. 64, 165-170 (1990).
**HKP**Chapter 9

#### 18 Oct

**HKP**Chapter 9

#### 20 Oct

- Ocular dominance column development: Analysis and simulation by Miller, Keller and Stryker.
- A dimension reduction framework for understanding cortical maps by R. Durbin and G. Mitchison.
- The cortical column: a structure without a function by Jonathan C. Horton and Daniel L. Adams

#### 25 Oct

- handout
- original Hopfield (1982) paper
- HKP Chapters 2 and 3

#### 27 Oct

- Hopfield (1984) paper
- Kechen Zhang paper on bump circuits
- Olshausen, Anderson & Van Essen, dynamic routing circuit model

#### 8 Nov

- Daugman paper on iris recognition
- A probability primer
- Bayesian probability theory and generative models

#### 15 Nov

- Mixture of Gaussians model
- Dayan & Abbott Chapter 10