Difference between revisions of "VS265: Reading"

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==== 27 Aug ====
==== 28 Aug ====
* Dreyfus, H.L. and Dreyfus, S.E. [http://redwood.berkeley.edu/vs265/DreyfusDreyfus.pdf ''Making a Mind vs. Modeling the Brain: Artificial Intelligence Back at a Branchpoint'']. Daedalus, Winter 1988.
* Dreyfus, H.L. and Dreyfus, S.E. [http://redwood.berkeley.edu/vs265/DreyfusDreyfus.pdf ''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) [http://dx.doi.org/10.1098/rstb.1999.0540 here] or [http://www.cnl.salk.edu/~tony/ptrsl.pdf here]
* Bell, A.J. ''Levels and loops: the future of artificial intelligence and neuroscience''. Phil Trans: Bio Sci. '''354''':2013--2020 (1999) [http://dx.doi.org/10.1098/rstb.1999.0540 here] or [http://www.cnl.salk.edu/~tony/ptrsl.pdf here]
* [http://www.aiai.ed.ac.uk/events/lighthill1973/1973-BBC-Lighthill-Controversy.mov 1973 Lighthill debate on future of AI]
* [http://www.aiai.ed.ac.uk/events/lighthill1973/1973-BBC-Lighthill-Controversy.mov 1973 Lighthill debate on future of AI]
==== 29 Aug ====
* Mead, C. [http://redwood.berkeley.edu/vs265/Mead-intro.pdf Chapter 1: Introduction] and [http://redwood.berkeley.edu/vs265/Mead-neurons.pdf Chapter 4: Neurons] from ''Analog VLSI and Neural Systems'', Addison-Wesley, 1989.
* [http://redwood.berkeley.edu/vs265/linear-neuron/linear-neuron-models.html Linear neuron models]
* [http://redwood.berkeley.edu/vs265/lti-conv/lti-convolution.html Linear time-invariant systems and convolution]
* [http://redwood.berkeley.edu/vs265/diffeq-sim/diffeq-sim.html Simulating differential equations]
* [http://redwood.berkeley.edu/vs265/dynamics/dynamics.html Dynamics]
* Carandini M, Heeger D (1994) [http://redwood.berkeley.edu/vs265/carandini-heeger.pdf Summation and division by neurons in primate visual cortex.]  Science, 264: 1333-1336.
* Land, MF and Fernald, RD. [http://redwood.berkeley.edu/vs265/landfernald92.pdf The Evolution of Eyes], Ann Revs Neuro, 1992.
* Zhang K, Sejnowski TJ (2000)  [http://redwood.berkeley.edu/vs265/zhang-sejnowski.pdf A universal scaling law between gray matter and white matter of cerebral cortex.]  PNAS, 97: 5621–5626.
==== 05 Sep ====
* Jordan, M.I. [http://redwood.berkeley.edu/vs265/PDP.pdf An Introduction to Linear Algebra in Parallel Distributed Processing] in McClelland and Rumelhart, ''Parallel Distributed Processing'', MIT Press, 1985.
* [http://redwood.berkeley.edu/vs265/linear-neuron/linear-neuron-models.html Linear neuron models]
* [http://redwood.berkeley.edu/vs265/linear-algebra/linear-algebra.html Linear algebra primer]
* [http://redwood.berkeley.edu/vs265/superlearn_handout1.pdf Handout] on supervised learning in single-stage feedforward networks
==== 17 Sep ====
* [http://redwood.berkeley.edu/vs265/superlearn_handout2.pdf Handout] on supervised learning in multi-layer feedforward networks - "backpropagation"
* Y. LeCun, L. Bottou, G. Orr, and K. Muller (1998) [http://redwood.berkeley.edu/vs265/lecun-98b.pdf  "Efficient BackProp,"]  in Neural Networks: Tricks of the trade, (G. Orr and Muller K., eds.).
* [http://cnl.salk.edu/Research/ParallelNetsPronounce/ NetTalk demo]
==== 24 Sep ====
* Handout: [http://redwood.berkeley.edu/vs265/hebb-pca-handout.pdf Hebbian learning and PCA]
* '''HKP''' Chapters 8 and 9
* '''PDP''' [http://redwood.berkeley.edu/vs265/chap9.pdf Chapter 9] (full text of Michael Jordan's tutorial on linear algebra, including section on eigenvectors)
* 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.
==== 8 Oct ====
* Barlow, HB. [http://redwood.berkeley.edu/vs265/barlow1972.pdf Single units and sensation: A neuron doctrine for perceptual psychology?]  Perception, volume 1, pp. 371 -394 (1972)
* Foldiak, P. [http://redwood.berkeley.edu/vs265/foldiak90.pdf Forming sparse representations by local anti-Hebbian learning]. Biol. Cybern. 64, 165-170 (1990).
* Olshausen BA, Field DJ. [http://redwood.berkeley.edu/vs265/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/vs265/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/vs265/simoncelli01-reprint.pdf Natural Image Statistics and Neural Representation], Annu. Rev. Neurosci. 2001. 24:1193–216.
* van Hateren & Ruderman [http://redwood.berkeley.edu/vs265/vanhateren-ruderman98.pdf Independent component analysis of natural image sequences], Proc. R. Soc. Lond. B (1998) 265. (blocked sparse coding/ICA of video)
* Olshausen BA [http://redwood.berkeley.edu/bruno/papers/icip03.pdf Sparse coding of time-varying natural images], ICIP 2003. (convolution sparse coding of video)
* Lewicki MS [http://www.cnbc.cmu.edu/cplab/papers/Lewicki-NatNeurosci-02.pdf Efficient coding of natural sounds], Nature Neuroscience, 5 (4): 356-363, 2002. (blocked sparse coding/ICA of sound)
* Smith E, Lewicki MS. [http://redwood.berkeley.edu/vs265/smith-lewicki-nature06.pdf Efficient auditory coding], Nature Vol 439 (2006).  (convolution sparse coding of sound)
==== 15 Oct ====
* [http://redwood.berkeley.edu/vs265/miller89.pdf Ocular dominance column development: Analysis and simulation] by Miller, Keller and Stryker.
* [http://redwood.berkeley.edu/vs265/durbin-mitchison.pdf A dimension reduction framework for understanding cortical maps] by R. Durbin and G. Mitchison.
* [http://redwood.berkeley.edu/vs265/horton05.pdf 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, [http://redwood.berkeley.edu/vs265/blasdel1992.pdf 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: [http://www.weizmann.ac.il/brain/grinvald/]
- From Clay Reid's lab, [http://www.nature.com/nature/journal/v433/n7026/abs/nature03274.html 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) [http://reid.med.harvard.edu/movies.html].
==== 22 Oct ====
* [http://redwood.berkeley.edu/vs265/tenenbaum-manifold.pdf A Global Geometric Framework for Nonlinear Dimensionality Reduction ], Tenenbaum et al., Science 2000.
* [http://redwood.berkeley.edu/vs265/roweis-saul-manifold.pdf Nonlinear Dimensionality Reduction by Locally Linear Embedding], Roweis and Saul, Science 2000.
* [http://redwood.berkeley.edu/vs265/carlsson-ijcv08.pdf On the Local Behavior of Spaces of Natural Images], Carlsson et al., Int J Comput Vis (2008) 76: 1–12.
Additional reading:
* [http://redwood.berkeley.edu/vs265/webster-face-adaptation.pdf Adaptation to natural facial categories], Michael A. Webster, Daniel Kaping, Yoko Mizokami & Paul Duhamel, Nature, 2004.
* [http://redwood.berkeley.edu/vs265/leopold.pdf Prototype-referenced shape encoding revealed by high-level aftereffects], David A. Leopold, Alice J. O’Toole, Thomas Vetter and Volker Blanz, Nature, 2001.
* [http://redwood.berkeley.edu/vs265/Blanz-siggraph-99.pdf A Morphable Model For The Synthesis Of 3D Faces], Blanz & Vetter 1999.
* [http://mbthompson.com/research/ Matthew B. Thompson's web page on flashed face distortion effect]
==== 24 Oct ====
* [http://redwood.berkeley.edu/vs265/attractor-networks.pdf Handout] on attractor neural networks
* [http://redwood.berkeley.edu/vs265/hopfield82.pdf original Hopfield (1982) paper]
* [http://redwood.berkeley.edu/vs265/hopfield84.pdf Hopfield (1984) paper]
* [http://redwood.berkeley.edu/vs265/marr-poggio-science76.pdf Marr-Poggio stereo algorithm paper]
* [http://redwood.berkeley.edu/vs265/zhang96.pdf Kechen Zhang paper on bump circuits]
* [http://redwood.berkeley.edu/vs265/olshausen-etal93.pdf Olshausen, Anderson & Van Essen, dynamic routing circuit model]
* HKP Chapters 2 and 3
==== 29 Oct ====
Chris Hillar guest lecture:
* [http://www.msri.org/people/members/chillar/files/mpf_hopfield.pdf Efficient and Optimal Binary Hopfield Associative Memory Storage Using Minimum Probability Flow]
* [http://www.msri.org/people/members/chillar/files/arxiv_prepaper.pdf Robust exponential binary pattern storage in Little-Hopfield networks]
* [http://www.cs.berkeley.edu/~malik/papers/SM-ncut.pdf NP-Hard Discrete Quadratic Optimization going into image segmentation (Shi, Malik 2000)]
==== 5 Nov ====
* [http://redwood.berkeley.edu/vs265/probability.pdf A probability primer]
* [http://redwood.berkeley.edu/vs265/bayes-prob.pdf Bayesian probability theory and generative models]
* [http://redwood.berkeley.edu/vs265/mog.pdf Mixture of Gaussians model ]
* T.J. Loredo, [http://redwood.berkeley.edu/vs265/loredo-laplace-supernova.pdf 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,[http://www.nature.com/nature/journal/v440/n7087/full/nature04701.html 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, [http://www.jneurosci.org/cgi/content/abstract/26/32/8254 The structure of multi-neuron firing patterns in primate retina], J Neurosci 260 (32) (2006), pp. 8254-8266.
==== 21 Nov ====
* [http://redwood.berkeley.edu/vs265/info-theory.pdf Information theory primer]
* [http://redwood.berkeley.edu/vs265/handout-sparse-08.pdf Sparse coding and ICA handout]
* Jascha Sohl-Dickstein, [http://redwood.berkeley.edu/vs265/jascha-natgrad.pdf Natural gradients made quick and dirty]
* Jascha Sohl-Dickstein, [http://redwood.berkeley.edu/vs265/jascha-cookbook.pdf Natural gradient cookbook]
* Bell & Sejnowski, [http://redwood.berkeley.edu/vs265/tony-ica.pdf An Information-Maximization Approach to Blind Separation and Blind Deconvolution], Neural Comp, 1995.
* Karklin & Simoncelli, [[http://redwood.berkeley.edu/vs265/karklin-simoncelli.pdf Efficient coding of natural images with a population of noisy Linear-Nonlinear neurons], NIPS 2011.
* Hyvarinen, Hoyer, Inki, [http://redwood.berkeley.edu/vs265/TICA.pdf Topographic Independent Component Analysis], Neural Comp, 2001.
* Karklin & Lewicki paper on  [http://redwood.berkeley.edu/vs265/karklin-lewicki2003.pdf Learning Higher-Order Structure in Natural Images], Network 2003.
* Shao & Cottrell paper on [http://redwood.berkeley.edu/vs265/hshan-nips06.pdf Recursive ICA], NIPS 2006.
==== 26 Nov ====
* Robbie Jacobs' [http://www.bcs.rochester.edu/people/robbie/jacobslab/cheat_sheet/sensoryIntegration.pdf notes on Kalman filter]
* [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]
* [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]
==== 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]

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28 Aug

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