VS265: Reading Fall2010: Difference between revisions
From RedwoodCenter
Jump to navigationJump to search
(→21 Sep) |
No edit summary |
||
| Line 32: | Line 32: | ||
* Atick, Redlich. [http://redwood.berkeley.edu/vs265/Atick-Redlich-NC92.pdf What does the retina know about natural scenes?], Neural Computation, 1992. | * Atick, Redlich. [http://redwood.berkeley.edu/vs265/Atick-Redlich-NC92.pdf What does the retina know about natural scenes?], Neural Computation, 1992. | ||
* Dan, Atick, Reid. [http://www.jneurosci.org/cgi/reprint/16/10/3351.pdf Efficient Coding of Natural Scenes in the Lateral Geniculate Nucleus: Experimental Test of a Computational Theory], J Neuroscience, 1996. | * Dan, Atick, Reid. [http://www.jneurosci.org/cgi/reprint/16/10/3351.pdf Efficient Coding of Natural Scenes in the Lateral Geniculate Nucleus: Experimental Test of a Computational Theory], J Neuroscience, 1996. | ||
==== 28 Sep ==== | |||
* Foldiak, P. [http://redwood.berkeley.edu/amir/vs298/foldiak90.pdf Forming sparse representations by local anti-Hebbian learning]. Biol. Cybern. 64, 165-170 (1990). | |||
* Olshausen BA, Field DJ. [http://redwood.berkeley.edu/amir/vs298/bruno-nature.pdf Emergence of simple-cell receptive field properties by learning a sparse code for natural images], Nature, 381: 607-609. (1996) | |||
Optional readings: | |||
* Rozell, Johnson, Baraniuk, Olshausen. [http://redwood.berkeley.edu/amir/vs298/rozell-sparse-coding-nc08.pdf Sparse Coding via Thresholding and Local Competition in Neural Circuits], Neural Computation 20, 2526–2563 (2008). | |||
* Simoncelli, Olshausen. [http://redwood.berkeley.edu/amir/vs298/simoncelli01-reprint.pdf Natural Image Statistics and Neural Representation], Annu. Rev. Neurosci. 2001. 24:1193–216. | |||
* Smith, Lewicki. [http://redwood.berkeley.edu/amir/vs298/smith-lewicki-nature06.pdf Efficient auditory coding], Nature Vol 439 (2006). | |||
<!--A handout on sparse coding and on 'ICA', something we haven't yet discussed: | |||
* [http://redwood.berkeley.edu/amir/vs298/sparse-coding-handout.pdf Sparse coding and 'ICA' ]--> | |||
<!--Dayan and Abbott has a nice section on sparse coding in Chapter 10. This is on the syllabus for unsupervised learning already, but you may want to focus on section 10.3 and 10.4.--> | |||
<!--Here is a link to [http://www.dsp.ece.rice.edu/cs/ Compressive Sensive Resources] at Rice. It has an enormous number of recent papers related to compressed sensing and sparse coding.--> | |||
Revision as of 21:30, 28 September 2010
26 Aug
- Dreyfus, H.L. and Dreyfus, S.E. Making a Mind vs. Modeling the Brain: Artificial Intelligence Back at a Branchpoint. Daedalus, Winter 1988.
- Bell, A.J. Levels and loops: the future of artificial intelligence and neuroscience. Phil Trans: Bio Sci. 354:2013--2020 (1999) here or here
Optional:
- Land, MF and Fernald, RD. The Evolution of Eyes, Ann Revs Neuro, 1992.
31 Aug
- Mead, C. Chapter 1: Introduction and Chapter 4: Neurons from Analog VLSI and Neural Systems, Addison-Wesley, 1989.
- Linear time-invariant systems and convolution
- Simulating differential equations
- Dynamics
- Carandini M, Heeger D (1994) Summation and division by neurons in primate visual cortex. Science, 264: 1333-1336.
02 Sep
- Jordan, M.I. An Introduction to Linear Algebra in Parallel Distributed Processing in McClelland and Rumelhart, Parallel Distributed Processing, MIT Press, 1985.
- Linear neuron models
- Linear algebra primer
07 Sep
- Handout on supervised learning in single-stage feedforward networks
- 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
21 Sep
- Handout: Hebbian learning and PCA
- HKP Chapters 8 and 9
- PDP Chapter 9 (full text of Michael Jordan's tutorial on linear algebra, including section on eigenvectors)
Optional:
- Atick, Redlich. What does the retina know about natural scenes?, Neural Computation, 1992.
- Dan, Atick, Reid. Efficient Coding of Natural Scenes in the Lateral Geniculate Nucleus: Experimental Test of a Computational Theory, J Neuroscience, 1996.
28 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)
Optional readings:
- Rozell, Johnson, Baraniuk, Olshausen. Sparse Coding via Thresholding and Local Competition in Neural Circuits, Neural Computation 20, 2526–2563 (2008).
- Simoncelli, Olshausen. Natural Image Statistics and Neural Representation, Annu. Rev. Neurosci. 2001. 24:1193–216.
- Smith, Lewicki. Efficient auditory coding, Nature Vol 439 (2006).
