# VS265: Reading Fall2012: Difference between revisions

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* [http://redwood.berkeley.edu/vs265/kalman.m kalman.m] demo script | * [http://redwood.berkeley.edu/vs265/kalman.m kalman.m] demo script | ||

* Greg Welch's [http://www.cs.unc.edu/~welch/kalman/kalmanIntro.html tutorial on Kalman filter] | * Greg Welch's [http://www.cs.unc.edu/~welch/kalman/kalmanIntro.html tutorial on Kalman filter] | ||

* [http://vision.ucla.edu/~doretto/ | * [http://vision.ucla.edu/~doretto/projects/dynamic-textures.html Dynamic texture models] | ||

* Kevin Murphy's [http://redwood.berkeley.edu/vs265/murphy-hmm.pdf HMM tutorial] | * Kevin Murphy's [http://redwood.berkeley.edu/vs265/murphy-hmm.pdf HMM tutorial] | ||

==== 28 Nov ==== | |||

* Chris Eliasmith, Charlie Anderson, [http://books.google.com/books?id=J6jz9s4kbfIC Neural Engineering: Computation, Representation, and Dynamics in Neurobiological Systems], MIT Press, 2004. | |||

Chapter 4 will be emailed to the class. | |||

* Softky and Koch, [http://redwood.berkeley.edu/vs265/softky-koch-jn93.pdf The Highly Irregular Firing of Cortical Cells Is Inconsistent with Temporal Integration of Random EPSPs], J Neuroscience, January 1993, 13(1):334-350. | |||

* Mainen and Sejnowski, [http://redwood.berkeley.edu/vs265/mainen-sejnowski.pdf Reliability of Spike Timing in Neocortical Neurons], Science, Vol 268, 6 June 1995. | |||

* Shadlen and Newsome, [http://redwood.berkeley.edu/vs265/shadlen-newsome1.pdf Noise, neural codes and cortical organization], Curr Opin in Neur, 1994, 4:569-579. | |||

* Shadlen and Newsom, [http://redwood.berkeley.edu/vs265/shadlen-newsome1.pdf Is there a signal in the noise?], Current Opin in Neur, 1995, 5:248-250. | |||

* Softky, [http://redwood.berkeley.edu/vs265/softky-commentary.pdf Simple codes versus efficient codes], Current Opin in Neuro, 1995, 5:239-247. | |||

* Izhikevich, [http://redwood.berkeley.edu/vs265/izhikevich-nn03.pdf Simple model of spiking neurons], IEEE Trans Neur Networks, 14(6):2003. | |||

* Izhikevich, [http://redwood.berkeley.edu/vs265/izhikevich-which-nn04.pdf Which Model to Use for Cortical Spiking Neurons?], IEEE Trans Neur Networks, 15(5):2004. | |||

==== 3 Dec ==== | |||

David Zipser guest lecture: | |||

* HKP section 7.3 | |||

* [http://redwood.berkeley.edu/vs265/zipser-manual.pdf BPTT manual] | |||

==== 5 Dec ==== | |||

Pentti Kanerva guest lecture: | |||

* Kanerva, [http://redwood.berkeley.edu/vs265/kanerva09-hyperdimensional.pdf Hyperdimensional Computing] |

## Latest revision as of 18:47, 28 August 2014

#### 27 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 - 1973 Lighthill debate on future of AI

#### 29 Aug

- Mead, C. Chapter 1: Introduction and Chapter 4: Neurons from
*Analog VLSI and Neural Systems*, Addison-Wesley, 1989. - Linear neuron models
- 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.

Optional:

- Land, MF and Fernald, RD. The Evolution of Eyes, Ann Revs Neuro, 1992.
- Zhang K, Sejnowski TJ (2000) A universal scaling law between gray matter and white matter of cerebral cortex. PNAS, 97: 5621–5626.

#### 05 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
- Handout on supervised learning in single-stage feedforward networks

#### 17 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

#### 24 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.

#### 8 Oct

- Barlow, HB. Single units and sensation: A neuron doctrine for perceptual psychology? Perception, volume 1, pp. 371 -394 (1972)
- 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.
- van Hateren & Ruderman Independent component analysis of natural image sequences, Proc. R. Soc. Lond. B (1998) 265. (blocked sparse coding/ICA of video)
- Olshausen BA Sparse coding of time-varying natural images, ICIP 2003. (convolution sparse coding of video)
- Lewicki MS Efficient coding of natural sounds, Nature Neuroscience, 5 (4): 356-363, 2002. (blocked sparse coding/ICA of sound)
- Smith E, Lewicki MS. Efficient auditory coding, Nature Vol 439 (2006). (convolution sparse coding of sound)

#### 15 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

Here are some additional links to papers mentioned in lecture. Optional reading:

- Gary Blasdel, Orientation selectivity, preference, and continuity in monkey striate cortex., J Neurosci, 1992. Another source of many of nice images are in the galleries on Amiram Grinvald's site: [1]

- From Clay Reid's lab, Functional imaging with cellular resolution reveals precise micro-architecture in visual cortex. Make sure you look at the supplementary material and videos on their web site (seems partly broken) [2].

#### 22 Oct

- A Global Geometric Framework for Nonlinear Dimensionality Reduction , Tenenbaum et al., Science 2000.
- Nonlinear Dimensionality Reduction by Locally Linear Embedding, Roweis and Saul, Science 2000.
- On the Local Behavior of Spaces of Natural Images, Carlsson et al., Int J Comput Vis (2008) 76: 1–12.

Additional reading:

- Adaptation to natural facial categories, Michael A. Webster, Daniel Kaping, Yoko Mizokami & Paul Duhamel, Nature, 2004.
- Prototype-referenced shape encoding revealed by high-level aftereffects, David A. Leopold, Alice J. O’Toole, Thomas Vetter and Volker Blanz, Nature, 2001.
- A Morphable Model For The Synthesis Of 3D Faces, Blanz & Vetter 1999.
- Matthew B. Thompson's web page on flashed face distortion effect

#### 24 Oct

- Handout on attractor neural networks
- original Hopfield (1982) paper
- Hopfield (1984) paper
- Marr-Poggio stereo algorithm paper
- Kechen Zhang paper on bump circuits
- Olshausen, Anderson & Van Essen, dynamic routing circuit model
- HKP Chapters 2 and 3

#### 29 Oct

Chris Hillar guest lecture:

- Efficient and Optimal Binary Hopfield Associative Memory Storage Using Minimum Probability Flow
- Robust exponential binary pattern storage in Little-Hopfield networks
- NP-Hard Discrete Quadratic Optimization going into image segmentation (Shi, Malik 2000)

#### 5 Nov

- A probability primer
- Bayesian probability theory and generative models
- Mixture of Gaussians model
- T.J. Loredo, From Laplace to supernova SN1987A: Bayesian inference in astrophysics

#### 19 Nov

- HKP Chapter 7, section 7.1 (Boltzmann machines)

Application to neural data analysis:

- E. Schneidman, M.J. Berry, R. Segev and W. Bialek,Weak pairwise correlations imply strongly correlated network states in a neural population, Nature 4400 (7087) (2006), pp. 1007-1012.
- J. Shlens, G.D. Field, J.L. Gauthier, M.I. Grivich, D. Petrusca, A. Sher, A.M. Litke and E.J. Chichilnisky, The structure of multi-neuron firing patterns in primate retina, J Neurosci 260 (32) (2006), pp. 8254-8266.

#### 21 Nov

- Information theory primer
- Sparse coding and ICA handout
- Jascha Sohl-Dickstein, Natural gradients made quick and dirty
- Jascha Sohl-Dickstein, Natural gradient cookbook
- Bell & Sejnowski, An Information-Maximization Approach to Blind Separation and Blind Deconvolution, Neural Comp, 1995.
- Karklin & Simoncelli, [Efficient coding of natural images with a population of noisy Linear-Nonlinear neurons, NIPS 2011.
- Hyvarinen, Hoyer, Inki, Topographic Independent Component Analysis, Neural Comp, 2001.
- Karklin & Lewicki paper on Learning Higher-Order Structure in Natural Images, Network 2003.
- Shao & Cottrell paper on Recursive ICA, NIPS 2006.

#### 26 Nov

- Robbie Jacobs' notes on Kalman filter
- kalman.m demo script
- Greg Welch's tutorial on Kalman filter
- Dynamic texture models
- Kevin Murphy's HMM tutorial

#### 28 Nov

- Chris Eliasmith, Charlie Anderson, Neural Engineering: Computation, Representation, and Dynamics in Neurobiological Systems, MIT Press, 2004.

Chapter 4 will be emailed to the class.

- Softky and Koch, The Highly Irregular Firing of Cortical Cells Is Inconsistent with Temporal Integration of Random EPSPs, J Neuroscience, January 1993, 13(1):334-350.
- Mainen and Sejnowski, Reliability of Spike Timing in Neocortical Neurons, Science, Vol 268, 6 June 1995.
- Shadlen and Newsome, Noise, neural codes and cortical organization, Curr Opin in Neur, 1994, 4:569-579.
- Shadlen and Newsom, Is there a signal in the noise?, Current Opin in Neur, 1995, 5:248-250.
- Softky, Simple codes versus efficient codes, Current Opin in Neuro, 1995, 5:239-247.
- Izhikevich, Simple model of spiking neurons, IEEE Trans Neur Networks, 14(6):2003.
- Izhikevich, Which Model to Use for Cortical Spiking Neurons?, IEEE Trans Neur Networks, 15(5):2004.

#### 3 Dec

David Zipser guest lecture:

- HKP section 7.3
- BPTT manual

#### 5 Dec

Pentti Kanerva guest lecture:

- Kanerva, Hyperdimensional Computing