VS298: Reading - Revision history

Bruno at 07:21, 11 December 2008

2008-12-11T07:21:38Z

← Older revision		Revision as of 07:13, 11 December 2008
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	==== 4 Dec ====		==== 4 Dec ====

	* A.J. Bell, [http://redwood.berkeley.edu/amir/vs298/bell-cross-level.pdf Towards a Cross-Level Theory		* A.J. Bell, [http://redwood.berkeley.edu/amir/vs298/bell-cross-level.pdf Towards a Cross-Level Theory of Neural Learning].
	of Neural Learning].

Bruno at 07:20, 11 December 2008

2008-12-11T07:20:25Z

← Older revision		Revision as of 07:12, 11 December 2008
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	==== 4 Dec ====		==== 4 Dec ====

	* A.J. Bell [http://redwood.berkeley.edu/amir/vs298/bell-cross-level.pdf Towards a Cross-Level Theory		* A.J. Bell, [http://redwood.berkeley.edu/amir/vs298/bell-cross-level.pdf Towards a Cross-Level Theory
	of Neural Learning].		of Neural Learning].

Bruno at 07:19, 11 December 2008

2008-12-11T07:19:27Z

New page

==== 2 Sep ====

* 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]
* Dreyfus, H.L. and Dreyfus, S.E. [http://redwood.berkeley.edu/amir/vs298/DreyfusDreyfus.pdf ''Making a Mind vs. Modeling the Brain: Artificial Intelligence Back at a Branchpoint'']. Daedalus, Winter 1988.
* Mead, C. [http://redwood.berkeley.edu/amir/vs298/Mead.pdf Chapter 1: Introduction] and [http://redwood.berkeley.edu/amir/vs298/Neurons.pdf Chapter 4: Neurons] from ''Analog VLSI and Neural Systems'', Addison-Wesley, 1989.
* Jordan, M.I. [http://redwood.berkeley.edu/amir/vs298/PDP.pdf An Introduction to Linear Algebra in Parallel Distributed Processing] in McClelland and Rumelhart, ''Parallel Distributed Processing'', MIT Press, 1985.
* Zhang K, Sejnowski TJ (2000) [http://redwood.berkeley.edu/amir/vs298/zhang-sejnowski.pdf A universal scaling law between gray matter and white matter of cerebral cortex.] PNAS, 97: 5621–5626.

Optional:

* Land, MF and Fernald, RD. [http://connes.berkeley.edu/~amir/vs298/landfernald92.pdf The Evolution of Eyes], Ann Revs Neuro, 1992.

* Douglas, R and Martin, K. [http://connes.berkeley.edu/~amir/vs298/douglasmartin2007.pdf Recurrent neuronal circuits in the neocortex], Current Biology, 2007.

==== 04 Sep ====
* [http://redwood.berkeley.edu/amir/vs298/linear-neuron/linear-neuron-models.html Linear neuron models]
* [http://redwood.berkeley.edu/amir/vs298/lti-conv/lti-convolution.html Linear time-invariant systems and convolution]
* [http://redwood.berkeley.edu/amir/vs298/diffeq-sim/diffeq-sim.html Simulating differential equations]
* Carandini M, Heeger D (1994) [http://redwood.berkeley.edu/amir/vs298/carandini-heeger.pdf Summation and division by neurons in primate visual cortex.] Science, 264: 1333-1336.
Optional reading for more background:
* [http://redwood.berkeley.edu/amir/vs298/linear-algebra/linear-algebra.html Linear algebra primer]
* [http://redwood.berkeley.edu/amir/vs298/dynamics/dynamics.html Dynamics]

==== 16 Sep ====
* [http://redwood.berkeley.edu/amir/vs298/superlearn1.pdf Handout] on supervised learning in single-stage feedforward networks

==== 18 Sep ====
* [http://redwood.berkeley.edu/amir/vs298/superlearn2.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/amir/vs298/lecun-98b.pdf "Efficient BackProp,"] in Neural Networks: Tricks of the trade, (G. Orr and Muller K., eds.).
* [http://www.cnl.salk.edu/ParallelNetsPronounce/index.php NetTalk demo]

==== 23 Sep ====
* Handout: [http://redwood.berkeley.edu/amir/vs298/hebb-pca.pdf Hebbian learning and PCA]
* '''HKP''' Chapter 8
* '''PDP''' [http://redwood.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

Optional:

* Atick, Redlich. [http://connes.berkeley.edu/~amir/vs298/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.

==== 30 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)

==== 2 Oct ====
Optional readings that covers material in lecture in greater depth:

* 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).

==== 7 Oct ====


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.

==== 9 Oct ====

Here are a list of references for David Zipser's talk: [http://redwood.berkeley.edu/amir/vs298/backpropneuralref.pdf pdf]. David also suggested the following chapter in an upcoming book by Thomas J. Anastasio: [http://redwood.berkeley.edu/amir/vs298/zipserchap10.pdf pdf (waiting for approval to post)]

==== 14 Oct ====

* [http://redwood.berkeley.edu/amir/vs298/miller89.pdf Ocular dominance column development: Analysis and simulation] by Miller, Keller and Stryker.
* [http://redwood.berkeley.edu/amir/vs298/durbin-mitchison.pdf A dimension reduction framework for understanding cortical maps] by R. Durbin and G. Mitchison.
* [http://redwood.berkeley.edu/amir/vs298/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/amir/vs298/blasdel1992.pdf Differential Imaging of Ocular Dominance and Orientation Selectivity 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].

==== 16 Oct ====

* [http://redwood.berkeley.edu/amir/vs298/tenenbaum-manifold.pdf A Global Geometric Framework for Nonlinear Dimensionality Reduction ], Tenenbaum et al., Science 2000.

* [http://redwood.berkeley.edu/amir/vs298/roweis-saul-manifold.pdf Nonlinear Dimensionality Reduction by Locally Linear Embedding], Roweis and Saul, Science 2000.

* [http://redwood.berkeley.edu/amir/vs298/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/amir/vs298/webster-face-adaptation.pdf Adaptation to natural facial categories], Michael A. Webster, Daniel Kaping, Yoko Mizokami & Paul Duhamel, Nature, 2004.

* [http://redwood.berkeley.edu/amir/vs298/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.

==== 21 Oct ====

* [http://redwood.berkeley.edu/amir/vs298/attractor-networks.pdf Handout] on attractor neural networks
* [http://redwood.berkeley.edu/amir/vs298/hopfield82.pdf original Hopfield (1982) paper]
* HKP Chapters 2 and 3

==== 23 Oct ====

* [http://redwood.berkeley.edu/amir/vs298/hopfield84.pdf Hopfield (1984) paper]
* [http://redwood.berkeley.edu/amir/vs298/zhang96.pdf Kechen Zhang paper on bump circuits]
* [http://redwood.berkeley.edu/amir/vs298/olshausen-etal93.pdf Olshausen, Anderson & Van Essen, dynamic routing circuit model]

==== 30 Oct ====

* [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]

==== 4 Nov ====

* [http://redwood.berkeley.edu/amir/vs298/mog.pdf Mixture of Gaussians model ]
* HKP Chapter 7, section 7.1

==== 6 Nov ====

Some suggested readings for Jon Shlens' talk.

===== Reviews=====
* S.H. Nirenberg and J.D. Victor, [http://dx.doi.org/10.1016/j.conb.2007.07.002 Analyzing the activity of large populations of neurons: how tractable is the problem?], Curr Opin Neurobiol 17 (4) (2007), pp. 397--400.

* Shlens J, Rieke F, Chichilnisky E. [http://dx.doi.org/10.1016/j.conb.2008.09.010 Synchronized firing in the retina]. Curr Opin Neurobiol. 2008 Oct 27.

=====Theory=====
* S. Amari (2001) [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=930911&isnumber=20133 Information geometry on hierarchy of probability distributions]. IEEE Trans Inform Theory 47:1701-1711

* E. Schneidman, S. Still, M.J. Berry and W. Bialek, [http://prola.aps.org/pdf/PRL/v91/i23/e238701 Network information and connected correlations], Phys Rev Lett 91 (2003) 238701.

=====Experiments=====
* 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.

* Tang A, Jackson D, Hobbs J, Chen W, Smith JL, Patel H, Prieto A, Petrusca D, Grivich MI, Sher A, Hottowy P, Dabrowski W, Litke AM, Beggs JM. [http://www.jneurosci.org/cgi/content/abstract/28/2/505 A maximum entropy model applied to spatial and temporal correlations from cortical networks in vitro]. J Neurosci. 2008 Jan 9;28(2):505-18.

==== 18 Nov ====

* [http://redwood.berkeley.edu/amir/vs298/info-theory.pdf Information theory primer]
* [http://redwood.berkeley.edu/amir/vs298/handout-sparse-08.pdf Sparse coding and ICA handout]
* Bell and Sejnowski, [http://redwood.berkeley.edu/amir/vs298/tony-ica.pdf An Information-Maximization Approach to Blind Separation and Blind Deconvolution], Neural Comp, 1995.
* Hyvarinen, Hoyer, Inki, [http://redwood.berkeley.edu/amir/vs298/TICA.pdf Topographic Independent Component Analysis], Neural Comp, 2001.

==== 20 Nov ====

* Robbie Jacobs' [http://www.bcs.rochester.edu/people/robbie/jacobslab/cheat_sheet/sensoryIntegration.pdf notes on Kalman filter]
* Greg Welch's [http://www.cs.unc.edu/~welch/kalman/kalmanIntro.html tutorial on Kalman filter]
* [http://vision.ucla.edu/~doretto/research.html Dynamic texture models]
* Kevin Murphy's [https://redwood.berkeley.edu/amir/vs298/murphy-hmm.pdf HMM tutorial]

==== 25 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/amir/vs298/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/amir/vs298/mainen-sejnowski.pdf Reliability of Spike Timing in Neocortical Neurons], Science, Vol 268, 6 June 1995.
* Shadlen and Newsome, [http://redwood.berkeley.edu/amir/vs298/shadlen-newsome1.pdf Noise, neural codes and cortical organization], Curr Opin in Neur, 1994, 4:569-579.
* Shadlen and Newsom, [http://redwood.berkeley.edu/amir/vs298/shadlen-newsome1.pdf Is there a signal in the noise?], Current Opin in Neur, 1995, 5:248-250.
* Softky, [http://redwood.berkeley.edu/amir/vs298/softky-commentary.pdf Simple codes versus efficient codes], Current Opin in Neuro, 1995, 5:239-247.
* Izhikevich, [http://redwood.berkeley.edu/amir/vs298/izhikevich-nn03.pdf Simple model of spiking neurons], IEEE Trans Neur Networks, 14(6):2003.
* Izhikevich, [http://redwood.berkeley.edu/amir/vs298/izhikevich-which-nn04.pdf Which Model to Use for Cortical Spiking Neurons?], IEEE Trans Neur Networks, 15(5):2004.

==== 4 Dec ====

* A.J. Bell [http://redwood.berkeley.edu/amir/vs298/bell-cross-level.pdf Towards a Cross-Level Theory
of Neural Learning].

Bruno: /* 20 Nov */

2008-11-21T07:45:34Z

20 Nov

← Older revision		Revision as of 07:37, 21 November 2008
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	==== 20 Nov ====		==== 20 Nov ====

	* [http://www.bcs.rochester.edu/people/robbie/jacobslab/cheat_sheet/sensoryIntegration.pdf ~~Robbie Jacobs~~ notes on Kalman filter]		* Robbie Jacobs' [http://www.bcs.rochester.edu/people/robbie/jacobslab/cheat_sheet/sensoryIntegration.pdf notes on Kalman filter]
	* [~~https~~://~~redwood~~.~~berkeley~~.edu/~~amir~~/~~vs298~~/~~murphy-hmm~~.~~pdf Kevin Murphy's HMM~~ tutorial]		* Greg Welch's [http://www.cs.unc.edu/~welch/kalman/kalmanIntro.html tutorial on Kalman filter]
	* [http://vision.ucla.edu/~doretto/research.html Dynamic texture models]		* [http://vision.ucla.edu/~doretto/research.html Dynamic texture models]
	* [~~http~~://~~www~~.~~cs.unc~~.edu/~~~welch~~/~~kalman~~/~~kalmanIntro~~.~~html Greg Welch's~~ tutorial ~~on Kalman filter~~]		* Kevin Murphy's [https://redwood.berkeley.edu/amir/vs298/murphy-hmm.pdf HMM tutorial]

Bruno: /* 20 Nov */

2008-11-21T07:36:50Z

20 Nov

← Older revision		Revision as of 07:28, 21 November 2008
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	* [http://www.bcs.rochester.edu/people/robbie/jacobslab/cheat_sheet/sensoryIntegration.pdf Robbie Jacobs notes on Kalman filter]		* [http://www.bcs.rochester.edu/people/robbie/jacobslab/cheat_sheet/sensoryIntegration.pdf Robbie Jacobs notes on Kalman filter]
	* [https://redwood.berkeley.edu/amir/vs298/murphy-hmm.pdf Kevin Murphy's HMM tutorial]		* [https://redwood.berkeley.edu/amir/vs298/murphy-hmm.pdf Kevin Murphy's HMM tutorial]
			* [http://vision.ucla.edu/~doretto/research.html Dynamic texture models]
			* [http://www.cs.unc.edu/~welch/kalman/kalmanIntro.html Greg Welch's tutorial on Kalman filter]

Bruno at 07:21, 21 November 2008

2008-11-21T07:21:28Z

New page

==== 2 Sep ====

* 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]
* Dreyfus, H.L. and Dreyfus, S.E. [http://redwood.berkeley.edu/amir/vs298/DreyfusDreyfus.pdf ''Making a Mind vs. Modeling the Brain: Artificial Intelligence Back at a Branchpoint'']. Daedalus, Winter 1988.
* Mead, C. [http://redwood.berkeley.edu/amir/vs298/Mead.pdf Chapter 1: Introduction] and [http://redwood.berkeley.edu/amir/vs298/Neurons.pdf Chapter 4: Neurons] from ''Analog VLSI and Neural Systems'', Addison-Wesley, 1989.
* Jordan, M.I. [http://redwood.berkeley.edu/amir/vs298/PDP.pdf An Introduction to Linear Algebra in Parallel Distributed Processing] in McClelland and Rumelhart, ''Parallel Distributed Processing'', MIT Press, 1985.
* Zhang K, Sejnowski TJ (2000) [http://redwood.berkeley.edu/amir/vs298/zhang-sejnowski.pdf A universal scaling law between gray matter and white matter of cerebral cortex.] PNAS, 97: 5621–5626.

Optional:

* Land, MF and Fernald, RD. [http://connes.berkeley.edu/~amir/vs298/landfernald92.pdf The Evolution of Eyes], Ann Revs Neuro, 1992.

* Douglas, R and Martin, K. [http://connes.berkeley.edu/~amir/vs298/douglasmartin2007.pdf Recurrent neuronal circuits in the neocortex], Current Biology, 2007.

==== 04 Sep ====
* [http://redwood.berkeley.edu/amir/vs298/linear-neuron/linear-neuron-models.html Linear neuron models]
* [http://redwood.berkeley.edu/amir/vs298/lti-conv/lti-convolution.html Linear time-invariant systems and convolution]
* [http://redwood.berkeley.edu/amir/vs298/diffeq-sim/diffeq-sim.html Simulating differential equations]
* Carandini M, Heeger D (1994) [http://redwood.berkeley.edu/amir/vs298/carandini-heeger.pdf Summation and division by neurons in primate visual cortex.] Science, 264: 1333-1336.
Optional reading for more background:
* [http://redwood.berkeley.edu/amir/vs298/linear-algebra/linear-algebra.html Linear algebra primer]
* [http://redwood.berkeley.edu/amir/vs298/dynamics/dynamics.html Dynamics]

==== 16 Sep ====
* [http://redwood.berkeley.edu/amir/vs298/superlearn1.pdf Handout] on supervised learning in single-stage feedforward networks

==== 18 Sep ====
* [http://redwood.berkeley.edu/amir/vs298/superlearn2.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/amir/vs298/lecun-98b.pdf "Efficient BackProp,"] in Neural Networks: Tricks of the trade, (G. Orr and Muller K., eds.).
* [http://www.cnl.salk.edu/ParallelNetsPronounce/index.php NetTalk demo]

==== 23 Sep ====
* Handout: [http://redwood.berkeley.edu/amir/vs298/hebb-pca.pdf Hebbian learning and PCA]
* '''HKP''' Chapter 8
* '''PDP''' [http://redwood.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

Optional:

* Atick, Redlich. [http://connes.berkeley.edu/~amir/vs298/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.

==== 30 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)

==== 2 Oct ====
Optional readings that covers material in lecture in greater depth:

* 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).

==== 7 Oct ====


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.

==== 9 Oct ====

Here are a list of references for David Zipser's talk: [http://redwood.berkeley.edu/amir/vs298/backpropneuralref.pdf pdf]. David also suggested the following chapter in an upcoming book by Thomas J. Anastasio: [http://redwood.berkeley.edu/amir/vs298/zipserchap10.pdf pdf (waiting for approval to post)]

==== 14 Oct ====

* [http://redwood.berkeley.edu/amir/vs298/miller89.pdf Ocular dominance column development: Analysis and simulation] by Miller, Keller and Stryker.
* [http://redwood.berkeley.edu/amir/vs298/durbin-mitchison.pdf A dimension reduction framework for understanding cortical maps] by R. Durbin and G. Mitchison.
* [http://redwood.berkeley.edu/amir/vs298/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/amir/vs298/blasdel1992.pdf Differential Imaging of Ocular Dominance and Orientation Selectivity 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].

==== 16 Oct ====

* [http://redwood.berkeley.edu/amir/vs298/tenenbaum-manifold.pdf A Global Geometric Framework for Nonlinear Dimensionality Reduction ], Tenenbaum et al., Science 2000.

* [http://redwood.berkeley.edu/amir/vs298/roweis-saul-manifold.pdf Nonlinear Dimensionality Reduction by Locally Linear Embedding], Roweis and Saul, Science 2000.

* [http://redwood.berkeley.edu/amir/vs298/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/amir/vs298/webster-face-adaptation.pdf Adaptation to natural facial categories], Michael A. Webster, Daniel Kaping, Yoko Mizokami & Paul Duhamel, Nature, 2004.

* [http://redwood.berkeley.edu/amir/vs298/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.

==== 21 Oct ====

* [http://redwood.berkeley.edu/amir/vs298/attractor-networks.pdf Handout] on attractor neural networks
* [http://redwood.berkeley.edu/amir/vs298/hopfield82.pdf original Hopfield (1982) paper]
* HKP Chapters 2 and 3

==== 23 Oct ====

* [http://redwood.berkeley.edu/amir/vs298/hopfield84.pdf Hopfield (1984) paper]
* [http://redwood.berkeley.edu/amir/vs298/zhang96.pdf Kechen Zhang paper on bump circuits]
* [http://redwood.berkeley.edu/amir/vs298/olshausen-etal93.pdf Olshausen, Anderson & Van Essen, dynamic routing circuit model]

==== 30 Oct ====

* [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]

==== 4 Nov ====

* [http://redwood.berkeley.edu/amir/vs298/mog.pdf Mixture of Gaussians model ]
* HKP Chapter 7, section 7.1

==== 6 Nov ====

Some suggested readings for Jon Shlens' talk.

===== Reviews=====
* S.H. Nirenberg and J.D. Victor, [http://dx.doi.org/10.1016/j.conb.2007.07.002 Analyzing the activity of large populations of neurons: how tractable is the problem?], Curr Opin Neurobiol 17 (4) (2007), pp. 397--400.

* Shlens J, Rieke F, Chichilnisky E. [http://dx.doi.org/10.1016/j.conb.2008.09.010 Synchronized firing in the retina]. Curr Opin Neurobiol. 2008 Oct 27.

=====Theory=====
* S. Amari (2001) [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=930911&isnumber=20133 Information geometry on hierarchy of probability distributions]. IEEE Trans Inform Theory 47:1701-1711

* E. Schneidman, S. Still, M.J. Berry and W. Bialek, [http://prola.aps.org/pdf/PRL/v91/i23/e238701 Network information and connected correlations], Phys Rev Lett 91 (2003) 238701.

=====Experiments=====
* 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.

* Tang A, Jackson D, Hobbs J, Chen W, Smith JL, Patel H, Prieto A, Petrusca D, Grivich MI, Sher A, Hottowy P, Dabrowski W, Litke AM, Beggs JM. [http://www.jneurosci.org/cgi/content/abstract/28/2/505 A maximum entropy model applied to spatial and temporal correlations from cortical networks in vitro]. J Neurosci. 2008 Jan 9;28(2):505-18.

==== 18 Nov ====

* [http://redwood.berkeley.edu/amir/vs298/info-theory.pdf Information theory primer]
* [http://redwood.berkeley.edu/amir/vs298/handout-sparse-08.pdf Sparse coding and ICA handout]
* Bell and Sejnowski, [http://redwood.berkeley.edu/amir/vs298/tony-ica.pdf An Information-Maximization Approach to Blind Separation and Blind Deconvolution], Neural Comp, 1995.
* Hyvarinen, Hoyer, Inki, [http://redwood.berkeley.edu/amir/vs298/TICA.pdf Topographic Independent Component Analysis], Neural Comp, 2001.

==== 20 Nov ====

* [http://www.bcs.rochester.edu/people/robbie/jacobslab/cheat_sheet/sensoryIntegration.pdf Robbie Jacobs notes on Kalman filter]
* [https://redwood.berkeley.edu/amir/vs298/murphy-hmm.pdf Kevin Murphy's HMM tutorial]

Bruno at 06:38, 4 November 2008

2008-11-04T06:38:16Z

New page

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: [http://redwood.berkeley.edu/wiki/VS298:_Optional_Reading 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) [http://dx.doi.org/10.1098/rstb.1999.0540 here] or [http://www.cnl.salk.edu/~tony/ptrsl.pdf here]
* Dreyfus, H.L. and Dreyfus, S.E. [http://redwood.berkeley.edu/amir/vs298/DreyfusDreyfus.pdf ''Making a Mind vs. Modeling the Brain: Artificial Intelligence Back at a Branchpoint'']. Daedalus, Winter 1988.
* Mead, C. [http://redwood.berkeley.edu/amir/vs298/Mead.pdf Chapter 1: Introduction] and [http://redwood.berkeley.edu/amir/vs298/Neurons.pdf Chapter 4: Neurons] from ''Analog VLSI and Neural Systems'', Addison-Wesley, 1989.
* Jordan, M.I. [http://redwood.berkeley.edu/amir/vs298/PDP.pdf An Introduction to Linear Algebra in Parallel Distributed Processing] in McClelland and Rumelhart, ''Parallel Distributed Processing'', MIT Press, 1985.
* Zhang K, Sejnowski TJ (2000) [http://redwood.berkeley.edu/amir/vs298/zhang-sejnowski.pdf A universal scaling law between gray matter and white matter of cerebral cortex.] PNAS, 97: 5621–5626.

==== 04 Sep ====
* [http://redwood.berkeley.edu/amir/vs298/linear-neuron/linear-neuron-models.html Linear neuron models]
* [http://redwood.berkeley.edu/amir/vs298/lti-conv/lti-convolution.html Linear time-invariant systems and convolution]
* [http://redwood.berkeley.edu/amir/vs298/diffeq-sim/diffeq-sim.html Simulating differential equations]
* Carandini M, Heeger D (1994) [http://redwood.berkeley.edu/amir/vs298/carandini-heeger.pdf Summation and division by neurons in primate visual cortex.] Science, 264: 1333-1336.
Optional reading for more background:
* [http://redwood.berkeley.edu/amir/vs298/linear-algebra/linear-algebra.html Linear algebra primer]
* [http://redwood.berkeley.edu/amir/vs298/dynamics/dynamics.html Dynamics]

==== 16 Sep ====
* [http://redwood.berkeley.edu/amir/vs298/superlearn1.pdf Handout] on supervised learning in single-stage feedforward networks

==== 18 Sep ====
* [http://redwood.berkeley.edu/amir/vs298/superlearn2.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/amir/vs298/lecun-98b.pdf "Efficient BackProp,"] in Neural Networks: Tricks of the trade, (G. Orr and Muller K., eds.).
* [http://www.cnl.salk.edu/ParallelNetsPronounce/index.php NetTalk demo]

==== 23 Sep ====
* Handout: [http://redwood.berkeley.edu/amir/vs298/hebb-pca.pdf Hebbian learning and PCA]
* '''HKP''' Chapter 8
* '''PDP''' [http://redwood.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 ====
* 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)

==== 2 Oct ====
Optional readings that covers material in lecture in greater depth:

* 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).

==== 7 Oct ====

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.

==== 9 Oct ====

Here are a list of references for David Zipser's talk: [http://redwood.berkeley.edu/amir/vs298/backpropneuralref.pdf pdf]. David also suggested the following chapter in an upcoming book by Thomas J. Anastasio: [http://redwood.berkeley.edu/amir/vs298/zipserchap10.pdf pdf (waiting for approval to post)]

==== 14 Oct ====

* [http://redwood.berkeley.edu/amir/vs298/miller89.pdf Ocular dominance column development: Analysis and simulation] by Miller, Keller and Stryker.
* [http://redwood.berkeley.edu/amir/vs298/durbin-mitchison.pdf A dimension reduction framework for understanding cortical maps] by R. Durbin and G. Mitchison.
* [http://redwood.berkeley.edu/amir/vs298/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/amir/vs298/blasdel1992.pdf Differential Imaging of Ocular Dominance and Orientation Selectivity 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].

==== 16 Oct ====

* [http://redwood.berkeley.edu/amir/vs298/tenenbaum-manifold.pdf A Global Geometric Framework for Nonlinear Dimensionality Reduction ], Tenenbaum et al., Science 2000.

* [http://redwood.berkeley.edu/amir/vs298/roweis-saul-manifold.pdf Nonlinear Dimensionality Reduction by Locally Linear Embedding], Roweis and Saul, Science 2000.

* [http://redwood.berkeley.edu/amir/vs298/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/amir/vs298/webster-face-adaptation.pdf Adaptation to natural facial categories], Michael A. Webster, Daniel Kaping, Yoko Mizokami & Paul Duhamel, Nature, 2004.

* [http://redwood.berkeley.edu/amir/vs298/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.

==== 21 Oct ====

* [http://redwood.berkeley.edu/amir/vs298/attractor-networks.pdf Handout] on attractor neural networks
* [http://redwood.berkeley.edu/amir/vs298/hopfield82.pdf original Hopfield (1982) paper]
* HKP Chapters 2 and 3

==== 23 Oct ====

* [http://redwood.berkeley.edu/amir/vs298/hopfield84.pdf Hopfield (1984) paper]
* [http://redwood.berkeley.edu/amir/vs298/zhang96.pdf Kechen Zhang paper on bump circuits]
* [http://redwood.berkeley.edu/amir/vs298/olshausen-etal93.pdf Olshausen, Anderson & Van Essen, dynamic routing circuit model]

==== 30 Oct ====

* [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]

==== 4 Nov ====

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

Bruno: /* 21 Oct */

2008-10-23T21:16:31Z

21 Oct

← Older revision		Revision as of 21:08, 23 October 2008
Line 88:		Line 88:
	==== 21 Oct ====		==== 21 Oct ====

	* ~~Handout on~~ [http://connes.berkeley.edu/~amir/vs298/attractor-networks.pdf ~~Attractor~~ neural networks]		* [http://connes.berkeley.edu/~amir/vs298/attractor-networks.pdf Handout] on attractor neural networks
	* [http://connes.berkeley.edu/~amir/vs298/hopfield82.pdf original Hopfield (1982) paper]		* [http://connes.berkeley.edu/~amir/vs298/hopfield82.pdf original Hopfield (1982) paper]
	* HKP Chapters 2 and 3		* HKP Chapters 2 and 3

Bruno: /* 21 Oct */

2008-10-23T21:11:13Z

21 Oct

New page

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: [http://redwood.berkeley.edu/wiki/VS298:_Optional_Reading 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) [http://dx.doi.org/10.1098/rstb.1999.0540 here] or [http://www.cnl.salk.edu/~tony/ptrsl.pdf here]
* Dreyfus, H.L. and Dreyfus, S.E. [http://connes.berkeley.edu/~amir/vs298/DreyfusDreyfus.pdf ''Making a Mind vs. Modeling the Brain: Artificial Intelligence Back at a Branchpoint'']. Daedalus, Winter 1988.
* Mead, C. [http://connes.berkeley.edu/~amir/vs298/Mead.pdf Chapter 1: Introduction] and [http://connes.berkeley.edu/~amir/vs298/Neurons.pdf Chapter 4: Neurons] from ''Analog VLSI and Neural Systems'', Addison-Wesley, 1989.
* Jordan, M.I. [http://connes.berkeley.edu/~amir/vs298/PDP.pdf An Introduction to Linear Algebra in Parallel Distributed Processing] in McClelland and Rumelhart, ''Parallel Distributed Processing'', MIT Press, 1985.
* Zhang K, Sejnowski TJ (2000) [http://connes.berkeley.edu/~amir/vs298/zhang-sejnowski.pdf A universal scaling law between gray matter and white matter of cerebral cortex.] PNAS, 97: 5621–5626.

==== 04 Sep ====
* [http://connes.berkeley.edu/~amir/vs298/linear-neuron/linear-neuron-models.html Linear neuron models]
* [http://connes.berkeley.edu/~amir/vs298/lti-conv/lti-convolution.html Linear time-invariant systems and convolution]
* [http://connes.berkeley.edu/~amir/vs298/diffeq-sim/diffeq-sim.html Simulating differential equations]
* Carandini M, Heeger D (1994) [http://connes.berkeley.edu/~amir/vs298/carandini-heeger.pdf Summation and division by neurons in primate visual cortex.] Science, 264: 1333-1336.
Optional reading for more background:
* [http://connes.berkeley.edu/~amir/vs298/linear-algebra/linear-algebra.html Linear algebra primer]
* [http://connes.berkeley.edu/~amir/vs298/dynamics/dynamics.html Dynamics]

==== 16 Sep ====
* [http://connes.berkeley.edu/~amir/vs298/superlearn1.pdf Handout] on supervised learning in single-stage feedforward networks

==== 18 Sep ====
* [http://connes.berkeley.edu/~amir/vs298/superlearn2.pdf Handout] on supervised learning in multi-layer feedforward networks - "backpropagation"
* Y. LeCun, L. Bottou, G. Orr, and K. Muller (1998) [http://connes.berkeley.edu/~amir/vs298/lecun-98b.pdf "Efficient BackProp,"] in Neural Networks: Tricks of the trade, (G. Orr and Muller K., eds.).
* [http://www.cnl.salk.edu/ParallelNetsPronounce/index.php NetTalk demo]

==== 23 Sep ====
* Handout: [http://connes.berkeley.edu/~amir/vs298/hebb-pca.pdf Hebbian learning and PCA]
* '''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)

==== 25 Sep ====
* '''HKP''' Chapter 9

==== 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).
* Olshausen BA, Field DJ. [http://connes.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)

==== 2 Oct ====
Optional readings that covers material in lecture in greater depth:

* Rozell, Johnson, Baraniuk, Olshausen. [http://connes.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://connes.berkeley.edu/~amir/vs298/simoncelli01-reprint.pdf Natural Image Statistics and Neural Representation], Annu. Rev. Neurosci. 2001. 24:1193–216.

* Smith, Lewicki. [http://connes.berkeley.edu/~amir/vs298/smith-lewicki-nature06.pdf Efficient auditory coding], Nature Vol 439 (2006).

==== 7 Oct ====

A handout on sparse coding and on 'ICA', something we haven't yet discussed:
* [http://connes.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.

==== 9 Oct ====

Here are a list of references for David Zipser's talk: [http://connes.berkeley.edu/~amir/vs298/backpropneuralref.pdf pdf]. David also suggested the following chapter in an upcoming book by Thomas J. Anastasio: [http://connes.berkeley.edu/~amir/vs298/zipserchap10.pdf pdf (waiting for approval to post)]

==== 14 Oct ====

* [http://connes.berkeley.edu/~amir/vs298/miller89.pdf Ocular dominance column development: Analysis and simulation] by Miller, Keller and Stryker.
* [http://connes.berkeley.edu/~amir/vs298/durbin-mitchison.pdf A dimension reduction framework for understanding cortical maps] by R. Durbin and G. Mitchison.
* [http://connes.berkeley.edu/~amir/vs298/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://connes.berkeley.edu/~amir/vs298/blasdel1992.pdf Differential Imaging of Ocular Dominance and Orientation Selectivity 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].

==== 16 Oct ====

* [http://connes.berkeley.edu/~amir/vs298/tenenbaum-manifold.pdf A Global Geometric Framework for Nonlinear Dimensionality Reduction ], Tenenbaum et al., Science 2000.

* [http://connes.berkeley.edu/~amir/vs298/roweis-saul-manifold.pdf Nonlinear Dimensionality Reduction by Locally Linear Embedding], Roweis and Saul, Science 2000.

* [http://connes.berkeley.edu/~amir/vs298/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://connes.berkeley.edu/~amir/vs298/webster-face-adaptation.pdf Adaptation to natural facial categories], Michael A. Webster, Daniel Kaping, Yoko Mizokami & Paul Duhamel, Nature, 2004.

* [http://connes.berkeley.edu/~amir/vs298/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.

==== 21 Oct ====

* Handout on [http://connes.berkeley.edu/~amir/vs298/attractor-networks.pdf Attractor neural networks]
* [http://connes.berkeley.edu/~amir/vs298/hopfield82.pdf original Hopfield (1982) paper]
* HKP Chapters 2 and 3

==== 23 Oct ====

* [http://connes.berkeley.edu/~amir/vs298/hopfield84.pdf Hopfield (1984) paper]
* [http://connes.berkeley.edu/~amir/vs298/zhang96.pdf Kechen Zhang paper on bump circuits]
* [http://connes.berkeley.edu/~amir/vs298/olshausen-etal93.pdf Olshausen, Anderson & Van Essen, dynamic routing circuit model]

Bruno at 18:07, 2 October 2008

2008-10-02T18:07:03Z

← Older revision		Revision as of 17:59, 2 October 2008
Line 30:		Line 30:
	* '''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).
	* '''HKP''' Chapter 9
	* 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)