RedwoodCenter - User contributions [en]

TCN

2006-05-13T04:49:49Z

TCN: /* Time and Location */

== Topics in Computational Neuroscience ==

=== Overview ===
'''UPDATE:''' If you would like, please register for VS 298 Section 3, Course Control Number (CCN) 66487, for 1 unit, S/U. The class will later be cross listed under Neuroscience.

'''NOTE:''' ''This semester (Spring '06) we are having a trial run of the class, Topics in Computational Neuroscience. We hope that this will be an ongoing class that will cover new papers and topics each semester. This semester we are planning to cover a topic each week and choose about two papers for each topic. Topics and papers we skip this semester will be covered in future semesters.''

This class is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. The class will provide a broad survey of literature from theoretical and computational neuroscience. Readings for the class will be selected from the best papers in the field and will combine both seminal works and recent theories. The class is scheduled to meet for one session each week for 1.5 hours for each session.

=== E-mail List ===

redwood_tcn at lists.berkeley.edu

You can subscribe yourself via the web [http://list.berkeley.edu link] or by sending mail to: majordomo@lists.berkeley.edu that contains:

subscribe redwood_tcn

in the body of the message.

=== Guidelines for Presenting Papers ===
Each person that selects a paper should present, in about 10 minutes (no slides):
* an executive summary
* an outline of the key points, ideas, or contributions
* a description of the key figures
* what you took away from the paper
* some potential questions for discussion

=== Make a Topic or Paper Suggestion ===

[[Suggestion Board]] (To gain access send email to: cadieu at berkeley dot edu)

=== Readings for Next Meeting! (May 17th) ===
====Motor Systems Theory====

* Todorov E Direct cortical control of muscle activation in voluntary arm movements: a model. (2000) Nature Neuroscience 3(4): 391-398 [http://cogsci.ucsd.edu/~todorov/papers/mi.pdf link]

* Georgopoulos AP. Neuron. 1994 Aug;13(2):257-68. New concepts in generation of movement. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=8060612&dopt=Abstract link]

Suggested Further Reading:
* Todorov E. On the role of primary motor cortex in arm movement control (2003) In Progress in Motor Control III, ch 6, pp 125-166, Latash and Levin (eds), Human Kinetics [http://cogsci.ucsd.edu/~todorov/papers/MotorCortex.pdf link]

* Scott, S.H. (2004) Optimal feedback control and the neural basis of volitional motor control. Nature Reviews Neuroscience 5:532-546. [http://limb.biomed.queensu.ca/publications/optimal_feedback_control_and_the_neural_basis.pdf link]

=== Time and Location ===
5:00 PM on Wednesdays at Triple Rock.

=== Syllabus (Topics and Readings) ===

<big>✔</big> indicates papers we've read.

==== Recent papers ====

* <big>✔</big> David J. Foster and Matthew A. Wilson, Reverse replay of behavioural sequences in hippocampal place cells during the awake state, Nature 440, 680-683 (30 March 2006) [http://www.nature.com/nature/journal/v440/n7084/pdf/nature04587.pdf link]

* <big>✔</big> Guetig, Sompolinsky. The Tempotron: a neuron that learns spike timing-based decisions. Nature Neuroscience 2006; 9:420-428. [http://www.nature.com/neuro/journal/v9/n3/pdf/nn1643.pdf link]

* <big>✔</big> E. Smith and M. S. Lewicki, Efficient Auditory Coding, Nature, 439 (7079), 2006. [http://www.nature.com/nature/journal/v439/n7079/abs/nature04485.html link]

: News and Views: DeWeese and Zador, "Neurobiology: Efficiency measures". [http://www.nature.com/nature/journal/v439/n7079/full/439920a.html link]

* <big>✔</big> D. Y. Tsao, W. A. Freiwald, R. B. H. Tootell, M. S. Livingstone, A Cortical Region Consisting Entirely of Face-Selective Cells, Science 3 February 2006; Vol. 311. no. 5761, pp. 670 - 674. [http://www.sciencemag.org/cgi/content/full/311/5761/670 link]

: Perspectives: Kanwisher, "What's in a Face?". [http://www.sciencemag.org/cgi/content/full/311/5761/617 link]

* Harris Nover, Charles H. Anderson, and Gregory C. DeAngelis. A Logarithmic, Scale-Invariant Representation of Speed in Macaque Middle Temporal Area Accounts for Speed Discrimination Performance. J. Neurosci., Oct 2005; 25: 10049 - 10060 [http://www.jneurosci.org/cgi/content/full/25/43/10049?maxtoshow=&HITS=10&hits=10&RESULTFORMAT=&author1=anderson&searchid=1&FIRSTINDEX=0&resourcetype=HWCIT link]

==== Early Work ====

* <big>✔</big> K. A. C. Martin, The Pope and grandmother−a frog's-eye view of theory, Nature Neuroscience 3, 1169 (2000) [http://www.nature.com/neuro/journal/v3/n11s/full/nn1100_1169.html link]

* <big>✔</big> Lettvin, Maturana, McCulloch, Pitts, "What the Frog's Eye Tells the Frog's Brian" [http://jerome.lettvin.info/WhatTheFrogsEyeTellsTheFrogsBrain.pdf link]

* <big>✔</big> Barlow HB (1972) Single Neurons and Sensation: A neuron doctrine for perceptual psychology. Perception 1, 371-394. [http://redwood.berkeley.edu/~amir/pdf/Barlow1972.pdf link]

* Marvin Minsky, Steps Toward Artificial Intelligence [http://portal.acm.org/citation.cfm?id=216408.216442&coll=GUIDE&dl=GUIDE link]

* McCulloch and Pitts, “A logical calculus of the ideas immanent in nervous activity” (1943) [http://portal.acm.org/citation.cfm?coll=GUIDE&dl=GUIDE&id=104377 link]

* Marr, selection from Vision; Artificial Intelligence—a personal view, by David Marr [http://scholar.google.com/url?sa=U&q=https://dspace.mit.edu/handle/1721.1/5776%3Fmode%3Dsimple link]

* Readings from Dartmouth Conf. 1956 proceedings
* Rosenblatt, Frank (1962). Principles of neurodynamics. New York: Spartan. Cf. Rumelhart, D.E., J. L. McClelland and the PDP Research Group (1986). Parallel Distributed Processing vol. 1&2. Cambridge: MIT. [http://scholar.google.com/scholar?hl=en&lr=&q=rosenblatt+principles+of+neurodynamics&btnG=Search link]

* "Making a Mind Versus Modeling the Brain: Artificial Intelligence Back at a Branchpoint" (with H. Dreyfus),Daedulus, Winter 1988 [http://portal.acm.org/citation.cfm?id=63323.66521 link]

==== Coding ====

* <big>✔</big> Kreiman, G. Neural Coding: Computational and Biophysical Perspectives, Physics of Life Reviews, 2, 71-102, 2004. [http://dx.doi.org/10.1016/j.plrev.2004.06.001 link]

* <big>✔</big> Pouget, A, Dayan, P & Zemel, RS (2000). Information processing with population codes. Nature Reviews Neuroscience, 1 , 125-132. [http://www.nature.com/nrn/journal/v1/n2/full/nrn1100_125a_fs.html link]

* E. Smith and M. S. Lewicki, Efficient Auditory Coding, Nature, 439 (7079), 2006. [http://www.nature.com/nature/journal/v439/n7079/abs/nature04485.html link]

: News and Views: DeWeese and Zador, "Neurobiology: Efficiency measures" [http://www.nature.com/nature/journal/v439/n7079/full/439920a.html link]

* Olshausen BA, Field DJ. Sparse Coding of Sensory Inputs. Curr Op in Neurobiology, 14: 481-487 (2004). [http://redwood.psych.cornell.edu/papers/current-opinion.pdf link]

* Coding and computation with neural spike trains. W Bialek & A Zee, J. Stat. Phys. 59, 103–115 (1990). [http://www.princeton.edu/~wbialek/our_papers/bialek+zee_90.pdf link]

* Selection from: Spikes: Exploring the Neural Code. F Rieke, D Warland, R de Ruyter van Steveninck & W Bialek (MIT Press, Cambridge, 1997). [http://melvyl.cdlib.org/F/?func=find-b&base=CDL90&request=0-262-18174-6&find_code=020 link]

* <big>✔</big> H. Nover, C. H. Anderson, and G. C. DeAngelis, A Logarithmic, Scale-Invariant Representation of Speed in Macaque Middle Temporal Area Accounts for Speed Discrimination Performance, J. Neurosci., Oct 2005; 25: 10049 - 10060 [http://www.jneurosci.org/cgi/reprint/25/43/10049 link]

* <big>✔</big> Ray Singh and Chris Eliasmith, Higher-Dimensional Neurons Explain the Tuning and Dynamics of Working Memory Cells, J. Neurosci. 2006;26 3667-3678 [http://www.jneurosci.org/cgi/reprint/26/14/3667 link]

==== Cortical Microcircuit/Universal Cortical Algorithm ====

* <big>✔</big> Vernon Mountcastle (1978), "An Organizing Principle for Cerebral Function: The Unit Model and the Distributed System", The Mindful Brain (Gerald M. Edelman and Vernon B. Mountcastle, eds.) Cambridge, MA: MIT Press [http://redwood.berkeley.edu/~amir/pdf/Mountcastle78.pdf link]

* <big>✔</big> Rodney J. Douglas, Kevan A.C. Martin, Neural Circuits of the Neocortex, Annual Review of Neuroscience 2004 27, 419-451 [http://arjournals.annualreviews.org/doi/full/10.1146/annurev.neuro.27.070203.144152 link]

* <big>✔</big> Selection from: Hawkins, J., On Intelligence (Chapter 6) [http://redwood.berkeley.edu/~amir/pdf/HawkinsChap6.pdf link]

* <big>✔</big> Henry Markram, The Blue Brain Project, Nature Neuroscience, 7:153-160, 2006. [http://www.nature.com/nrn/journal/v7/n2/pdf/nrn1848.pdf link]

* Douglas RJ, Martin KAC Whitteridge D. (1989) A canonical microcircuit for neocortex. Neural Computation 1: 480-488. [http://scholar.google.com/scholar?hl=en&lr=&q=douglas+martin+whitteridge+neural+comput+1989&btnG=Search link] (the [http://www.archive.org/details/redwood_center_inaugural_symposium_08 related talk] at the Redwood symposium)

* Cross-modal plasticity in cortical development: differentiation and specification sensory cortex, by Mriganka Sur, Sarah L. Pallas and Anna W. Roe. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=1694329&dopt=Citation link]

* Poggio, T. and E. Bizzi. Generalization in Vision and Motor Control, Nature, Vol. 431, 768-774, 2004. [http://www.nature.com/nature/journal/v431/n7010/abs/nature03014.html link]

* Marr D, "A Theory for Cerebral Neocortex", Proc Roy Soc London(B), 176, 161-234, 1970. [http://adsabs.harvard.edu/abs/1970RSPSB.176..161M link]

==== Feedback, Hierarchical Organization, Generative Models ====

* <big>✔</big> David Mumford, Neuronal Architectures for Pattern-theoretic Problems, in "Large-Scale Neuronal Theories of the Brain", C.Koch & J.Davis, editors, MIT Press, 1994, pp.125-152. [http://redwood.berkeley.edu/~amir/pdf/Neuronal_Mumford.pdf link]

* <big>✔</big> RPN Rao. Bayesian inference and attention in the visual cortex. Neuroreport 16(16), 1843-1848, 2005. [http://www.cs.washington.edu/homes/rao/nreport_bayes_atten05.pdf link]

* TS Lee, D Mumford Hierarchical Bayesian inference in the visual cortex, Journal of the Optical Society of America A, 2003 [http://scholar.google.com/url?sa=U&q=http://www.dam.brown.edu/people/mumford/Papers/JOSALeeMumford.pdf link]

* Felleman, DJ and Van Essen, DC (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex, 1, 1-47. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=1822724&dopt=Abstract link]

==== Manifold Learning ====

* <big>✔</big> Richard Durbin & Graeme Mitchison, A dimension reduction framework for understanding cortical maps, Nature 343, 644 - 647 (15 February 1990) [http://www.nature.com/nature/journal/v343/n6259/abs/343644a0.html link]

* <big>✔</big> Tal Kenet, Dmitri Bibitchkov, Misha Tsodyks, Amiram Grinvald and Amos Arieli, Spontaneously emerging cortical representations of visual attributes, Nature 425, 954-956 (30 October 2003) [http://www.nature.com/nature/journal/v425/n6961/full/nature02078.html link]

* Selection from: Self-Organizing and Associative Memory, T Kohonen - Japanese translation 2nd Edition, Splinger-Verlag Tokyo, 1994 [http://citeseer.ist.psu.edu/context/2930/0 link]

* JB Tenenbaum, V de Silva, JC Langford, A Global Geometric Framework for Nonlinear Dimensionality Reduction, Science, 2000 [http://www.sciencemag.org/cgi/content/full/290/5500/2319 link]

* ST Roweis, LK Saul, Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science, 2000 [http://www.sciencemag.org/cgi/content/full/290/5500/2323 link]

* Nicholas V. Swindale, Doron Shoham, Amiram Grinvald, Tobias Bonhoeffer & Mark Hübener, Visual cortex maps are optimized for uniform coverage, Nature Neuroscience 3, 822 - 826 (2000) [http://www.nature.com/neuro/journal/v3/n8/full/nn0800_822.html link]

Background reading on SOMs
* Neural Computation and Self-Organizing Maps - An Introduction, by Helge Ritter, Thomas Martinetz, and Klaus Schulten, Addison-Wesley, New York, 1992, [http://www.ks.uiuc.edu/Services/Class/PHYS498TBP/spring2002/neuro_4.pdf Kohonen's Network Model] [http://www.ks.uiuc.edu/Services/Class/PHYS498TBP/spring2002/neuro_book.html Contents]

==== Plasticity, Hebbian Learning ====

* Dan, Y. and Poo, M.-m. (2004). Spike timing-dependent plasticity of neural circuits. Neuron 44, 23-30 [http://www.ling.sinica.edu.tw/paper-Mu-ming%2520Poo-2.pdf link]

* Abbott LF, Nelson SB. (2000) Synaptic plasticity: taming the beast. Nat Neurosci. 3 Suppl:1178-83. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=11127835&dopt=Abstract link]

* P. Foldiak, Forming sparse representations by local anti-Hebbian learning, Biological Cybernetics, vol. 64, pp. 165-170, 1990. [http://www.st-andrews.ac.uk/~pf2/FoldiakSparseBC90.pdf link]

* M. Tsodyks, Spike-timing-dependent synaptic plasticity–The long road towards understanding neuronal mechanisms Trends in Neuroscience, 2002. [http://dx.doi.org/10.1016/S0166-2236(02)02294-4 link]

* Saudargienne A, Porr B, and Worgotter F. How the shape of pre- and postsynaptic signals can influence STDP: a biophysical model. Neural Comp 16: 595–625, 2004. [http://neco.mitpress.org/cgi/content/abstract/16/3/595 link]

* Seung, HS (2000) Half a century of Hebb. Nat. Neurosc. Suppl: 1166. [http://www.nature.com/cgi-taf/DynaPage.taf?file=/neuro/journal/v3/n11s/full/nn1100_1166.html link]

* H. S. Seung. Learning in spiking neural networks by reinforcement of stochastic synaptic transmission. Neuron 40, 1063-1073 (2003). [http://hebb.mit.edu/people/seung/papers/Neuron18Dec03.pdf link]

* Hinton, G. E. and Sejnowski, T. J. (1986), Learning and relearning in Boltzmann machines. In Rumelhart, D. E. and McClelland, J. L., editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations, MIT Press, Cambridge, MA. [http://portal.acm.org/citation.cfm?coll=GUIDE&dl=GUIDE&id=104291 link]

==== Oscillations ====

* Gray, CM (1999) The temporal correlation hypothesis of visual feature integration: still alive and well. Neuron. 1999, 24(1):31-47, 111-25. Review. [http://scholar.google.com/url?sa=U&q=http://lifesci.rutgers.edu/~auerbach/Gray%2520binding.pdf link]

* Neuron Special Issue on Oscillations, 1999, 24(1) [http://www.sciencedirect.com/science?_ob=IssueURL&_tockey=%23TOC%237054%231999%23999759998%23575347%23FLA%23&_auth=y&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=fcf1d593bba077aaec2c4f34adbe071d link]

==== Associative Memory ====

* <big>✔</big> J Hopfield. Neural Networks and Physical Systems with Emergent Collective Computational Abilities. PNAS, 79:2554-2558 (1982) [http://www.pnas.org/cgi/content/abstract/79/8/2554 link]
** David MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge Univ. Press, 2003. [http://www.inference.phy.cam.ac.uk/mackay/itprnn/ps/504.520.pdf Chapter 42 (Hopfield networks)]

* <big>✔</big> S. Fusi, P.J. Drew, and L.F. Abbott, Cascade Models of Synaptically Stored Memories, Neuron 2005 45: 599-611. [http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B6WSS-4FH5K0X-H-1&_cdi=7054&_user=4420&_orig=search&_coverDate=02%2F17%2F2005&_qd=1&_sk=999549995&view=c&wchp=dGLbVlz-zSkWz&md5=1b45b90c5fb16f945ba490ba48503de7&ie=/sdarticle.pdf link]

* G. Palm. On the storage capacity of an associative memory with randomly distributed storage elements. Biol. Cybernetics. 36:19-31 (1980). [http://www.springerlink.com/link.asp?id=wp7q375127415311 link]

* Selection from: Self-Organizing and Associative Memory, T Kohonen - Japanese translation 2nd Edition, Splinger-Verlag Tokyo, 1994

==== Models of Invariance ====

* Olshausen BA, Anderson CH, Van Essen DC (1993). A Neurobiological Model of Visual Attention and Invariant Pattern Recognition Based on Dynamic Routing of Information, The Journal of Neuroscience, 13(11), 4700-4719. [http://redwood.berkeley.edu/bruno/papers/ link]

* K Fukushima, Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biological Cybernetics (Historical Archive), Volume 36, Issue 4, Apr 1980, Pages 193 - 202 [http://www.springerlink.com/link.asp?id=r6g5w3tt54528137 link]

* <big>✔</big> P. Foldiak, Learning invariance from transformation sequences, Neural Computation, vol. 3, pp. 194-200, 1991. [http://www.st-andrews.ac.uk/~pf2/FoldiakInvarianceLearningNC91.pdf link]

* <big>✔</big> L Wiskott. Slow Feature Analysis: Unsupervised Learning of Invariances. Neural Computation. 2002;14:715-770. [http://redwood.berkeley.edu/~cadieu/pdf/WiskottSejnowski-SlowFeature.pdf link]

==== Active Perception-sensorimotor loops ====

* Churchland P, Ramachandran VS, Sejnowski TJ. Large-Scale Neuronal Theories of the Brain: Chapter 2, Critique of Pure Vision, 1994. [http://cnl.salk.edu/~terry/PDF/PureVision.94.pdf link]

* Oregan JK, Noe A. A sensorimotor account of vision and visual consciousness, BBS (2001) 24:939-1031. [http://ist-socrates.berkeley.edu/~noe/oregan.noe.pdf link]

* Philipona D, O'Regan JK, Nadal JP. Is There Something Out There? Inferring Space from Sensorimotor Dependencies. Neural Computation 2003;15:2029-2049. [http://neco.mitpress.org/cgi/content/abstract/15/9/2029 link]

====Birdsong====

* Anthony Leonardo & Michale S. Fee. Ensemble Coding of Vocal Control in Birdsong. The Journal of Neuroscience, January 19, 2005, 25(3):652-661. [http://www.jneurosci.org/cgi/reprint/25/3/652 link (Warning: 27.7 MB .pdf)]

* Troyer TW, Bottjer SW. Birdsong: models and mechanisms. Curr Opin Neurobiol. 2001 Dec;11(6):721-6. [http://redwood.berkeley.edu/~cadieu/pdf/TroyerBottjer_Birdsong_CurrOpinNeuro01.pdf link]

==== Theories of the Ventral Stream ====

* <big>✔</big> Ullman, S. (1995). Sequence-seeking and counter streams: A computational model for bi-directional information flow in the visual cortex. Cerebral Cortex, 5(1) 1-11. [http://redwood.berkeley.edu/~cadieu/pdf/UllmanCerCor95.pdf link]

* <big>✔</big> Riesenhuber, M., and T. Poggio. Models of Object Recognition, Nature Neuroscience, 3 Supp., 1199-1204, 2000. [http://www.nature.com/neuro/journal/v3/n11s/pdf/nn1100_1199.pdf link]

* Riesenhuber, M. and T. Poggio. How Visual Cortex Recognizes Objects: The Tale of the Standard Model. In: The Visual Neurosciences, (Eds. L.M. Chalupa and J.S. Werner), MIT Press, Cambridge, MA, Vol. 2, 1640-1653, 2003. [http://cbcl.mit.edu/projects/cbcl/publications/ps/max-vis-cortex-03.pdf link]

* Rolls,E.T. (1997) A neurophysiological and computational approach to the functions of the temporal lobe cortical visual areas in invariant object recognition. Chapter 9, pp. 184-220 in Computational and Psychophysical Mechanisms of Visual Coding, eds. M.Jenkin and L.Harris. Cambridge University Press: Cambridge. [http://www.cns.ox.ac.uk/publications.html link]

====Sleep====
* <big>✔</big> Crick, Francis, and Graeme Mitchison. "The Function of Dream Sleep." Nature 304, (14 July 1983): 111-114. [http://redwood.berkeley.edu/~cadieu/pdf/CrickMitchisonDreamSleep1983.pdf link]

* <big>✔</big> Sutherland GR, McNaughton B., Memory trace reactivation in hippocampal and neocortical neuronal ensembles., Curr Opin Neurobiol. 2000 Apr;10(2):180-6 [http://redwood.berkeley.edu/~cadieu/pdf/SutherlandMcNaughtonMemoryReactivation1999.pdf link]

==== Theories of Hippocampus ====

* Becker, S. (2005) "A computational principle for hippocampal learning and neurogenesis". Hippocampus 15(6):722-738. [http://www.science.mcmaster.ca/Psychology/sb.html link]

* Leutgeb, S., Leutgeb, J.K., Moser, M.-B., and Moser, E.I. (2005). Place cells, spatial maps and the population code for memory. Current Opinion in Neurobiology, 15, 738-746. [http://www.cbm.ntnu.no/publications link]

* [http://www.bris.ac.uk/synaptic/research/projects/memory/spatialmem.htm place cells]

* Hasselmo, M.E. and McClelland, J.L. (1999) Neural models of memory. Curr. Opinion Neurobiol. 9: 184-188. [http://people.bu.edu/hasselmo/HasselmoMcClelland.pdf link]

==== Motor System ====

* Körding, KP. and Wolpert, D. (2004) Bayesian Integration in Sensorimotor Learning, Nature 427:244-247. [http://www.koerding.com/pubs/koerdingNature2004.pdf link]

* Todorov E Direct cortical control of muscle activation in voluntary arm movements: a model. (2000) Nature Neuroscience 3(4): 391-398 [http://cogsci.ucsd.edu/~todorov/papers/mi.pdf link]

* Todorov E. On the role of primary motor cortex in arm movement control (2003) In Progress in Motor Control III, ch 6, pp 125-166, Latash and Levin (eds), Human Kinetics [http://cogsci.ucsd.edu/~todorov/papers/MotorCortex.pdf link]

* Georgopoulos AP. Neuron. 1994 Aug;13(2):257-68. New concepts in generation of movement. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=8060612&dopt=Abstract link]

* Scott, S.H. (2004) Optimal feedback control and the neural basis of volitional motor control. Nature Reviews Neuroscience 5:532-546. [http://limb.biomed.queensu.ca/publications/optimal_feedback_control_and_the_neural_basis.pdf link]

TCN

2006-05-13T04:47:51Z

TCN: /* Readings for Next Meeting! (May 17th) */

== Topics in Computational Neuroscience ==

=== Overview ===
'''UPDATE:''' If you would like, please register for VS 298 Section 3, Course Control Number (CCN) 66487, for 1 unit, S/U. The class will later be cross listed under Neuroscience.

'''NOTE:''' ''This semester (Spring '06) we are having a trial run of the class, Topics in Computational Neuroscience. We hope that this will be an ongoing class that will cover new papers and topics each semester. This semester we are planning to cover a topic each week and choose about two papers for each topic. Topics and papers we skip this semester will be covered in future semesters.''

This class is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. The class will provide a broad survey of literature from theoretical and computational neuroscience. Readings for the class will be selected from the best papers in the field and will combine both seminal works and recent theories. The class is scheduled to meet for one session each week for 1.5 hours for each session.

=== E-mail List ===

redwood_tcn at lists.berkeley.edu

You can subscribe yourself via the web [http://list.berkeley.edu link] or by sending mail to: majordomo@lists.berkeley.edu that contains:

subscribe redwood_tcn

in the body of the message.

=== Guidelines for Presenting Papers ===
Each person that selects a paper should present, in about 10 minutes (no slides):
* an executive summary
* an outline of the key points, ideas, or contributions
* a description of the key figures
* what you took away from the paper
* some potential questions for discussion

=== Make a Topic or Paper Suggestion ===

[[Suggestion Board]] (To gain access send email to: cadieu at berkeley dot edu)

=== Readings for Next Meeting! (May 17th) ===
====Motor Systems Theory====

* Todorov E Direct cortical control of muscle activation in voluntary arm movements: a model. (2000) Nature Neuroscience 3(4): 391-398 [http://cogsci.ucsd.edu/~todorov/papers/mi.pdf link]

* Georgopoulos AP. Neuron. 1994 Aug;13(2):257-68. New concepts in generation of movement. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=8060612&dopt=Abstract link]

Suggested Further Reading:
* Todorov E. On the role of primary motor cortex in arm movement control (2003) In Progress in Motor Control III, ch 6, pp 125-166, Latash and Levin (eds), Human Kinetics [http://cogsci.ucsd.edu/~todorov/papers/MotorCortex.pdf link]

* Scott, S.H. (2004) Optimal feedback control and the neural basis of volitional motor control. Nature Reviews Neuroscience 5:532-546. [http://limb.biomed.queensu.ca/publications/optimal_feedback_control_and_the_neural_basis.pdf link]

=== Time and Location ===
7:00 PM on Wednesdays in the Beach room, 3105 Tolman (the doors on the east side of the building should be open).

=== Syllabus (Topics and Readings) ===

<big>✔</big> indicates papers we've read.

==== Recent papers ====

* <big>✔</big> David J. Foster and Matthew A. Wilson, Reverse replay of behavioural sequences in hippocampal place cells during the awake state, Nature 440, 680-683 (30 March 2006) [http://www.nature.com/nature/journal/v440/n7084/pdf/nature04587.pdf link]

* <big>✔</big> Guetig, Sompolinsky. The Tempotron: a neuron that learns spike timing-based decisions. Nature Neuroscience 2006; 9:420-428. [http://www.nature.com/neuro/journal/v9/n3/pdf/nn1643.pdf link]

* <big>✔</big> E. Smith and M. S. Lewicki, Efficient Auditory Coding, Nature, 439 (7079), 2006. [http://www.nature.com/nature/journal/v439/n7079/abs/nature04485.html link]

: News and Views: DeWeese and Zador, "Neurobiology: Efficiency measures". [http://www.nature.com/nature/journal/v439/n7079/full/439920a.html link]

* <big>✔</big> D. Y. Tsao, W. A. Freiwald, R. B. H. Tootell, M. S. Livingstone, A Cortical Region Consisting Entirely of Face-Selective Cells, Science 3 February 2006; Vol. 311. no. 5761, pp. 670 - 674. [http://www.sciencemag.org/cgi/content/full/311/5761/670 link]

: Perspectives: Kanwisher, "What's in a Face?". [http://www.sciencemag.org/cgi/content/full/311/5761/617 link]

* Harris Nover, Charles H. Anderson, and Gregory C. DeAngelis. A Logarithmic, Scale-Invariant Representation of Speed in Macaque Middle Temporal Area Accounts for Speed Discrimination Performance. J. Neurosci., Oct 2005; 25: 10049 - 10060 [http://www.jneurosci.org/cgi/content/full/25/43/10049?maxtoshow=&HITS=10&hits=10&RESULTFORMAT=&author1=anderson&searchid=1&FIRSTINDEX=0&resourcetype=HWCIT link]

==== Early Work ====

* <big>✔</big> K. A. C. Martin, The Pope and grandmother−a frog's-eye view of theory, Nature Neuroscience 3, 1169 (2000) [http://www.nature.com/neuro/journal/v3/n11s/full/nn1100_1169.html link]

* <big>✔</big> Lettvin, Maturana, McCulloch, Pitts, "What the Frog's Eye Tells the Frog's Brian" [http://jerome.lettvin.info/WhatTheFrogsEyeTellsTheFrogsBrain.pdf link]

* <big>✔</big> Barlow HB (1972) Single Neurons and Sensation: A neuron doctrine for perceptual psychology. Perception 1, 371-394. [http://redwood.berkeley.edu/~amir/pdf/Barlow1972.pdf link]

* Marvin Minsky, Steps Toward Artificial Intelligence [http://portal.acm.org/citation.cfm?id=216408.216442&coll=GUIDE&dl=GUIDE link]

* McCulloch and Pitts, “A logical calculus of the ideas immanent in nervous activity” (1943) [http://portal.acm.org/citation.cfm?coll=GUIDE&dl=GUIDE&id=104377 link]

* Marr, selection from Vision; Artificial Intelligence—a personal view, by David Marr [http://scholar.google.com/url?sa=U&q=https://dspace.mit.edu/handle/1721.1/5776%3Fmode%3Dsimple link]

* Readings from Dartmouth Conf. 1956 proceedings
* Rosenblatt, Frank (1962). Principles of neurodynamics. New York: Spartan. Cf. Rumelhart, D.E., J. L. McClelland and the PDP Research Group (1986). Parallel Distributed Processing vol. 1&2. Cambridge: MIT. [http://scholar.google.com/scholar?hl=en&lr=&q=rosenblatt+principles+of+neurodynamics&btnG=Search link]

* "Making a Mind Versus Modeling the Brain: Artificial Intelligence Back at a Branchpoint" (with H. Dreyfus),Daedulus, Winter 1988 [http://portal.acm.org/citation.cfm?id=63323.66521 link]

==== Coding ====

* <big>✔</big> Kreiman, G. Neural Coding: Computational and Biophysical Perspectives, Physics of Life Reviews, 2, 71-102, 2004. [http://dx.doi.org/10.1016/j.plrev.2004.06.001 link]

* <big>✔</big> Pouget, A, Dayan, P & Zemel, RS (2000). Information processing with population codes. Nature Reviews Neuroscience, 1 , 125-132. [http://www.nature.com/nrn/journal/v1/n2/full/nrn1100_125a_fs.html link]

* E. Smith and M. S. Lewicki, Efficient Auditory Coding, Nature, 439 (7079), 2006. [http://www.nature.com/nature/journal/v439/n7079/abs/nature04485.html link]

: News and Views: DeWeese and Zador, "Neurobiology: Efficiency measures" [http://www.nature.com/nature/journal/v439/n7079/full/439920a.html link]

* Olshausen BA, Field DJ. Sparse Coding of Sensory Inputs. Curr Op in Neurobiology, 14: 481-487 (2004). [http://redwood.psych.cornell.edu/papers/current-opinion.pdf link]

* Coding and computation with neural spike trains. W Bialek & A Zee, J. Stat. Phys. 59, 103–115 (1990). [http://www.princeton.edu/~wbialek/our_papers/bialek+zee_90.pdf link]

* Selection from: Spikes: Exploring the Neural Code. F Rieke, D Warland, R de Ruyter van Steveninck & W Bialek (MIT Press, Cambridge, 1997). [http://melvyl.cdlib.org/F/?func=find-b&base=CDL90&request=0-262-18174-6&find_code=020 link]

* <big>✔</big> H. Nover, C. H. Anderson, and G. C. DeAngelis, A Logarithmic, Scale-Invariant Representation of Speed in Macaque Middle Temporal Area Accounts for Speed Discrimination Performance, J. Neurosci., Oct 2005; 25: 10049 - 10060 [http://www.jneurosci.org/cgi/reprint/25/43/10049 link]

* <big>✔</big> Ray Singh and Chris Eliasmith, Higher-Dimensional Neurons Explain the Tuning and Dynamics of Working Memory Cells, J. Neurosci. 2006;26 3667-3678 [http://www.jneurosci.org/cgi/reprint/26/14/3667 link]

==== Cortical Microcircuit/Universal Cortical Algorithm ====

* <big>✔</big> Vernon Mountcastle (1978), "An Organizing Principle for Cerebral Function: The Unit Model and the Distributed System", The Mindful Brain (Gerald M. Edelman and Vernon B. Mountcastle, eds.) Cambridge, MA: MIT Press [http://redwood.berkeley.edu/~amir/pdf/Mountcastle78.pdf link]

* <big>✔</big> Rodney J. Douglas, Kevan A.C. Martin, Neural Circuits of the Neocortex, Annual Review of Neuroscience 2004 27, 419-451 [http://arjournals.annualreviews.org/doi/full/10.1146/annurev.neuro.27.070203.144152 link]

* <big>✔</big> Selection from: Hawkins, J., On Intelligence (Chapter 6) [http://redwood.berkeley.edu/~amir/pdf/HawkinsChap6.pdf link]

* <big>✔</big> Henry Markram, The Blue Brain Project, Nature Neuroscience, 7:153-160, 2006. [http://www.nature.com/nrn/journal/v7/n2/pdf/nrn1848.pdf link]

* Douglas RJ, Martin KAC Whitteridge D. (1989) A canonical microcircuit for neocortex. Neural Computation 1: 480-488. [http://scholar.google.com/scholar?hl=en&lr=&q=douglas+martin+whitteridge+neural+comput+1989&btnG=Search link] (the [http://www.archive.org/details/redwood_center_inaugural_symposium_08 related talk] at the Redwood symposium)

* Cross-modal plasticity in cortical development: differentiation and specification sensory cortex, by Mriganka Sur, Sarah L. Pallas and Anna W. Roe. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=1694329&dopt=Citation link]

* Poggio, T. and E. Bizzi. Generalization in Vision and Motor Control, Nature, Vol. 431, 768-774, 2004. [http://www.nature.com/nature/journal/v431/n7010/abs/nature03014.html link]

* Marr D, "A Theory for Cerebral Neocortex", Proc Roy Soc London(B), 176, 161-234, 1970. [http://adsabs.harvard.edu/abs/1970RSPSB.176..161M link]

==== Feedback, Hierarchical Organization, Generative Models ====

* <big>✔</big> David Mumford, Neuronal Architectures for Pattern-theoretic Problems, in "Large-Scale Neuronal Theories of the Brain", C.Koch & J.Davis, editors, MIT Press, 1994, pp.125-152. [http://redwood.berkeley.edu/~amir/pdf/Neuronal_Mumford.pdf link]

* <big>✔</big> RPN Rao. Bayesian inference and attention in the visual cortex. Neuroreport 16(16), 1843-1848, 2005. [http://www.cs.washington.edu/homes/rao/nreport_bayes_atten05.pdf link]

* TS Lee, D Mumford Hierarchical Bayesian inference in the visual cortex, Journal of the Optical Society of America A, 2003 [http://scholar.google.com/url?sa=U&q=http://www.dam.brown.edu/people/mumford/Papers/JOSALeeMumford.pdf link]

* Felleman, DJ and Van Essen, DC (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex, 1, 1-47. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=1822724&dopt=Abstract link]

==== Manifold Learning ====

* <big>✔</big> Richard Durbin & Graeme Mitchison, A dimension reduction framework for understanding cortical maps, Nature 343, 644 - 647 (15 February 1990) [http://www.nature.com/nature/journal/v343/n6259/abs/343644a0.html link]

* <big>✔</big> Tal Kenet, Dmitri Bibitchkov, Misha Tsodyks, Amiram Grinvald and Amos Arieli, Spontaneously emerging cortical representations of visual attributes, Nature 425, 954-956 (30 October 2003) [http://www.nature.com/nature/journal/v425/n6961/full/nature02078.html link]

* Selection from: Self-Organizing and Associative Memory, T Kohonen - Japanese translation 2nd Edition, Splinger-Verlag Tokyo, 1994 [http://citeseer.ist.psu.edu/context/2930/0 link]

* JB Tenenbaum, V de Silva, JC Langford, A Global Geometric Framework for Nonlinear Dimensionality Reduction, Science, 2000 [http://www.sciencemag.org/cgi/content/full/290/5500/2319 link]

* ST Roweis, LK Saul, Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science, 2000 [http://www.sciencemag.org/cgi/content/full/290/5500/2323 link]

* Nicholas V. Swindale, Doron Shoham, Amiram Grinvald, Tobias Bonhoeffer & Mark Hübener, Visual cortex maps are optimized for uniform coverage, Nature Neuroscience 3, 822 - 826 (2000) [http://www.nature.com/neuro/journal/v3/n8/full/nn0800_822.html link]

Background reading on SOMs
* Neural Computation and Self-Organizing Maps - An Introduction, by Helge Ritter, Thomas Martinetz, and Klaus Schulten, Addison-Wesley, New York, 1992, [http://www.ks.uiuc.edu/Services/Class/PHYS498TBP/spring2002/neuro_4.pdf Kohonen's Network Model] [http://www.ks.uiuc.edu/Services/Class/PHYS498TBP/spring2002/neuro_book.html Contents]

==== Plasticity, Hebbian Learning ====

* Dan, Y. and Poo, M.-m. (2004). Spike timing-dependent plasticity of neural circuits. Neuron 44, 23-30 [http://www.ling.sinica.edu.tw/paper-Mu-ming%2520Poo-2.pdf link]

* Abbott LF, Nelson SB. (2000) Synaptic plasticity: taming the beast. Nat Neurosci. 3 Suppl:1178-83. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=11127835&dopt=Abstract link]

* P. Foldiak, Forming sparse representations by local anti-Hebbian learning, Biological Cybernetics, vol. 64, pp. 165-170, 1990. [http://www.st-andrews.ac.uk/~pf2/FoldiakSparseBC90.pdf link]

* M. Tsodyks, Spike-timing-dependent synaptic plasticity–The long road towards understanding neuronal mechanisms Trends in Neuroscience, 2002. [http://dx.doi.org/10.1016/S0166-2236(02)02294-4 link]

* Saudargienne A, Porr B, and Worgotter F. How the shape of pre- and postsynaptic signals can influence STDP: a biophysical model. Neural Comp 16: 595–625, 2004. [http://neco.mitpress.org/cgi/content/abstract/16/3/595 link]

* Seung, HS (2000) Half a century of Hebb. Nat. Neurosc. Suppl: 1166. [http://www.nature.com/cgi-taf/DynaPage.taf?file=/neuro/journal/v3/n11s/full/nn1100_1166.html link]

* H. S. Seung. Learning in spiking neural networks by reinforcement of stochastic synaptic transmission. Neuron 40, 1063-1073 (2003). [http://hebb.mit.edu/people/seung/papers/Neuron18Dec03.pdf link]

* Hinton, G. E. and Sejnowski, T. J. (1986), Learning and relearning in Boltzmann machines. In Rumelhart, D. E. and McClelland, J. L., editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations, MIT Press, Cambridge, MA. [http://portal.acm.org/citation.cfm?coll=GUIDE&dl=GUIDE&id=104291 link]

==== Oscillations ====

* Gray, CM (1999) The temporal correlation hypothesis of visual feature integration: still alive and well. Neuron. 1999, 24(1):31-47, 111-25. Review. [http://scholar.google.com/url?sa=U&q=http://lifesci.rutgers.edu/~auerbach/Gray%2520binding.pdf link]

* Neuron Special Issue on Oscillations, 1999, 24(1) [http://www.sciencedirect.com/science?_ob=IssueURL&_tockey=%23TOC%237054%231999%23999759998%23575347%23FLA%23&_auth=y&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=fcf1d593bba077aaec2c4f34adbe071d link]

==== Associative Memory ====

* <big>✔</big> J Hopfield. Neural Networks and Physical Systems with Emergent Collective Computational Abilities. PNAS, 79:2554-2558 (1982) [http://www.pnas.org/cgi/content/abstract/79/8/2554 link]
** David MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge Univ. Press, 2003. [http://www.inference.phy.cam.ac.uk/mackay/itprnn/ps/504.520.pdf Chapter 42 (Hopfield networks)]

* <big>✔</big> S. Fusi, P.J. Drew, and L.F. Abbott, Cascade Models of Synaptically Stored Memories, Neuron 2005 45: 599-611. [http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B6WSS-4FH5K0X-H-1&_cdi=7054&_user=4420&_orig=search&_coverDate=02%2F17%2F2005&_qd=1&_sk=999549995&view=c&wchp=dGLbVlz-zSkWz&md5=1b45b90c5fb16f945ba490ba48503de7&ie=/sdarticle.pdf link]

* G. Palm. On the storage capacity of an associative memory with randomly distributed storage elements. Biol. Cybernetics. 36:19-31 (1980). [http://www.springerlink.com/link.asp?id=wp7q375127415311 link]

* Selection from: Self-Organizing and Associative Memory, T Kohonen - Japanese translation 2nd Edition, Splinger-Verlag Tokyo, 1994

==== Models of Invariance ====

* Olshausen BA, Anderson CH, Van Essen DC (1993). A Neurobiological Model of Visual Attention and Invariant Pattern Recognition Based on Dynamic Routing of Information, The Journal of Neuroscience, 13(11), 4700-4719. [http://redwood.berkeley.edu/bruno/papers/ link]

* K Fukushima, Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biological Cybernetics (Historical Archive), Volume 36, Issue 4, Apr 1980, Pages 193 - 202 [http://www.springerlink.com/link.asp?id=r6g5w3tt54528137 link]

* <big>✔</big> P. Foldiak, Learning invariance from transformation sequences, Neural Computation, vol. 3, pp. 194-200, 1991. [http://www.st-andrews.ac.uk/~pf2/FoldiakInvarianceLearningNC91.pdf link]

* <big>✔</big> L Wiskott. Slow Feature Analysis: Unsupervised Learning of Invariances. Neural Computation. 2002;14:715-770. [http://redwood.berkeley.edu/~cadieu/pdf/WiskottSejnowski-SlowFeature.pdf link]

==== Active Perception-sensorimotor loops ====

* Churchland P, Ramachandran VS, Sejnowski TJ. Large-Scale Neuronal Theories of the Brain: Chapter 2, Critique of Pure Vision, 1994. [http://cnl.salk.edu/~terry/PDF/PureVision.94.pdf link]

* Oregan JK, Noe A. A sensorimotor account of vision and visual consciousness, BBS (2001) 24:939-1031. [http://ist-socrates.berkeley.edu/~noe/oregan.noe.pdf link]

* Philipona D, O'Regan JK, Nadal JP. Is There Something Out There? Inferring Space from Sensorimotor Dependencies. Neural Computation 2003;15:2029-2049. [http://neco.mitpress.org/cgi/content/abstract/15/9/2029 link]

====Birdsong====

* Anthony Leonardo & Michale S. Fee. Ensemble Coding of Vocal Control in Birdsong. The Journal of Neuroscience, January 19, 2005, 25(3):652-661. [http://www.jneurosci.org/cgi/reprint/25/3/652 link (Warning: 27.7 MB .pdf)]

* Troyer TW, Bottjer SW. Birdsong: models and mechanisms. Curr Opin Neurobiol. 2001 Dec;11(6):721-6. [http://redwood.berkeley.edu/~cadieu/pdf/TroyerBottjer_Birdsong_CurrOpinNeuro01.pdf link]

==== Theories of the Ventral Stream ====

* <big>✔</big> Ullman, S. (1995). Sequence-seeking and counter streams: A computational model for bi-directional information flow in the visual cortex. Cerebral Cortex, 5(1) 1-11. [http://redwood.berkeley.edu/~cadieu/pdf/UllmanCerCor95.pdf link]

* <big>✔</big> Riesenhuber, M., and T. Poggio. Models of Object Recognition, Nature Neuroscience, 3 Supp., 1199-1204, 2000. [http://www.nature.com/neuro/journal/v3/n11s/pdf/nn1100_1199.pdf link]

* Riesenhuber, M. and T. Poggio. How Visual Cortex Recognizes Objects: The Tale of the Standard Model. In: The Visual Neurosciences, (Eds. L.M. Chalupa and J.S. Werner), MIT Press, Cambridge, MA, Vol. 2, 1640-1653, 2003. [http://cbcl.mit.edu/projects/cbcl/publications/ps/max-vis-cortex-03.pdf link]

* Rolls,E.T. (1997) A neurophysiological and computational approach to the functions of the temporal lobe cortical visual areas in invariant object recognition. Chapter 9, pp. 184-220 in Computational and Psychophysical Mechanisms of Visual Coding, eds. M.Jenkin and L.Harris. Cambridge University Press: Cambridge. [http://www.cns.ox.ac.uk/publications.html link]

====Sleep====
* <big>✔</big> Crick, Francis, and Graeme Mitchison. "The Function of Dream Sleep." Nature 304, (14 July 1983): 111-114. [http://redwood.berkeley.edu/~cadieu/pdf/CrickMitchisonDreamSleep1983.pdf link]

* <big>✔</big> Sutherland GR, McNaughton B., Memory trace reactivation in hippocampal and neocortical neuronal ensembles., Curr Opin Neurobiol. 2000 Apr;10(2):180-6 [http://redwood.berkeley.edu/~cadieu/pdf/SutherlandMcNaughtonMemoryReactivation1999.pdf link]

==== Theories of Hippocampus ====

* Becker, S. (2005) "A computational principle for hippocampal learning and neurogenesis". Hippocampus 15(6):722-738. [http://www.science.mcmaster.ca/Psychology/sb.html link]

* Leutgeb, S., Leutgeb, J.K., Moser, M.-B., and Moser, E.I. (2005). Place cells, spatial maps and the population code for memory. Current Opinion in Neurobiology, 15, 738-746. [http://www.cbm.ntnu.no/publications link]

* [http://www.bris.ac.uk/synaptic/research/projects/memory/spatialmem.htm place cells]

* Hasselmo, M.E. and McClelland, J.L. (1999) Neural models of memory. Curr. Opinion Neurobiol. 9: 184-188. [http://people.bu.edu/hasselmo/HasselmoMcClelland.pdf link]

==== Motor System ====

* Körding, KP. and Wolpert, D. (2004) Bayesian Integration in Sensorimotor Learning, Nature 427:244-247. [http://www.koerding.com/pubs/koerdingNature2004.pdf link]

* Todorov E Direct cortical control of muscle activation in voluntary arm movements: a model. (2000) Nature Neuroscience 3(4): 391-398 [http://cogsci.ucsd.edu/~todorov/papers/mi.pdf link]

* Todorov E. On the role of primary motor cortex in arm movement control (2003) In Progress in Motor Control III, ch 6, pp 125-166, Latash and Levin (eds), Human Kinetics [http://cogsci.ucsd.edu/~todorov/papers/MotorCortex.pdf link]

* Georgopoulos AP. Neuron. 1994 Aug;13(2):257-68. New concepts in generation of movement. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=8060612&dopt=Abstract link]

* Scott, S.H. (2004) Optimal feedback control and the neural basis of volitional motor control. Nature Reviews Neuroscience 5:532-546. [http://limb.biomed.queensu.ca/publications/optimal_feedback_control_and_the_neural_basis.pdf link]

TCN

2006-05-13T04:46:27Z

TCN: /* Motor System */

== Topics in Computational Neuroscience ==

=== Overview ===
'''UPDATE:''' If you would like, please register for VS 298 Section 3, Course Control Number (CCN) 66487, for 1 unit, S/U. The class will later be cross listed under Neuroscience.

'''NOTE:''' ''This semester (Spring '06) we are having a trial run of the class, Topics in Computational Neuroscience. We hope that this will be an ongoing class that will cover new papers and topics each semester. This semester we are planning to cover a topic each week and choose about two papers for each topic. Topics and papers we skip this semester will be covered in future semesters.''

This class is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. The class will provide a broad survey of literature from theoretical and computational neuroscience. Readings for the class will be selected from the best papers in the field and will combine both seminal works and recent theories. The class is scheduled to meet for one session each week for 1.5 hours for each session.

=== E-mail List ===

redwood_tcn at lists.berkeley.edu

You can subscribe yourself via the web [http://list.berkeley.edu link] or by sending mail to: majordomo@lists.berkeley.edu that contains:

subscribe redwood_tcn

in the body of the message.

=== Guidelines for Presenting Papers ===
Each person that selects a paper should present, in about 10 minutes (no slides):
* an executive summary
* an outline of the key points, ideas, or contributions
* a description of the key figures
* what you took away from the paper
* some potential questions for discussion

=== Make a Topic or Paper Suggestion ===

[[Suggestion Board]] (To gain access send email to: cadieu at berkeley dot edu)

=== Readings for Next Meeting! (May 17th) ===
====Motor Systems Theory====
* TBD

=== Time and Location ===
7:00 PM on Wednesdays in the Beach room, 3105 Tolman (the doors on the east side of the building should be open).

=== Syllabus (Topics and Readings) ===

<big>✔</big> indicates papers we've read.

==== Recent papers ====

* <big>✔</big> David J. Foster and Matthew A. Wilson, Reverse replay of behavioural sequences in hippocampal place cells during the awake state, Nature 440, 680-683 (30 March 2006) [http://www.nature.com/nature/journal/v440/n7084/pdf/nature04587.pdf link]

* <big>✔</big> Guetig, Sompolinsky. The Tempotron: a neuron that learns spike timing-based decisions. Nature Neuroscience 2006; 9:420-428. [http://www.nature.com/neuro/journal/v9/n3/pdf/nn1643.pdf link]

* <big>✔</big> E. Smith and M. S. Lewicki, Efficient Auditory Coding, Nature, 439 (7079), 2006. [http://www.nature.com/nature/journal/v439/n7079/abs/nature04485.html link]

: News and Views: DeWeese and Zador, "Neurobiology: Efficiency measures". [http://www.nature.com/nature/journal/v439/n7079/full/439920a.html link]

* <big>✔</big> D. Y. Tsao, W. A. Freiwald, R. B. H. Tootell, M. S. Livingstone, A Cortical Region Consisting Entirely of Face-Selective Cells, Science 3 February 2006; Vol. 311. no. 5761, pp. 670 - 674. [http://www.sciencemag.org/cgi/content/full/311/5761/670 link]

: Perspectives: Kanwisher, "What's in a Face?". [http://www.sciencemag.org/cgi/content/full/311/5761/617 link]

* Harris Nover, Charles H. Anderson, and Gregory C. DeAngelis. A Logarithmic, Scale-Invariant Representation of Speed in Macaque Middle Temporal Area Accounts for Speed Discrimination Performance. J. Neurosci., Oct 2005; 25: 10049 - 10060 [http://www.jneurosci.org/cgi/content/full/25/43/10049?maxtoshow=&HITS=10&hits=10&RESULTFORMAT=&author1=anderson&searchid=1&FIRSTINDEX=0&resourcetype=HWCIT link]

==== Early Work ====

* <big>✔</big> K. A. C. Martin, The Pope and grandmother−a frog's-eye view of theory, Nature Neuroscience 3, 1169 (2000) [http://www.nature.com/neuro/journal/v3/n11s/full/nn1100_1169.html link]

* <big>✔</big> Lettvin, Maturana, McCulloch, Pitts, "What the Frog's Eye Tells the Frog's Brian" [http://jerome.lettvin.info/WhatTheFrogsEyeTellsTheFrogsBrain.pdf link]

* <big>✔</big> Barlow HB (1972) Single Neurons and Sensation: A neuron doctrine for perceptual psychology. Perception 1, 371-394. [http://redwood.berkeley.edu/~amir/pdf/Barlow1972.pdf link]

* Marvin Minsky, Steps Toward Artificial Intelligence [http://portal.acm.org/citation.cfm?id=216408.216442&coll=GUIDE&dl=GUIDE link]

* McCulloch and Pitts, “A logical calculus of the ideas immanent in nervous activity” (1943) [http://portal.acm.org/citation.cfm?coll=GUIDE&dl=GUIDE&id=104377 link]

* Marr, selection from Vision; Artificial Intelligence—a personal view, by David Marr [http://scholar.google.com/url?sa=U&q=https://dspace.mit.edu/handle/1721.1/5776%3Fmode%3Dsimple link]

* Readings from Dartmouth Conf. 1956 proceedings
* Rosenblatt, Frank (1962). Principles of neurodynamics. New York: Spartan. Cf. Rumelhart, D.E., J. L. McClelland and the PDP Research Group (1986). Parallel Distributed Processing vol. 1&2. Cambridge: MIT. [http://scholar.google.com/scholar?hl=en&lr=&q=rosenblatt+principles+of+neurodynamics&btnG=Search link]

* "Making a Mind Versus Modeling the Brain: Artificial Intelligence Back at a Branchpoint" (with H. Dreyfus),Daedulus, Winter 1988 [http://portal.acm.org/citation.cfm?id=63323.66521 link]

==== Coding ====

* <big>✔</big> Kreiman, G. Neural Coding: Computational and Biophysical Perspectives, Physics of Life Reviews, 2, 71-102, 2004. [http://dx.doi.org/10.1016/j.plrev.2004.06.001 link]

* <big>✔</big> Pouget, A, Dayan, P & Zemel, RS (2000). Information processing with population codes. Nature Reviews Neuroscience, 1 , 125-132. [http://www.nature.com/nrn/journal/v1/n2/full/nrn1100_125a_fs.html link]

* E. Smith and M. S. Lewicki, Efficient Auditory Coding, Nature, 439 (7079), 2006. [http://www.nature.com/nature/journal/v439/n7079/abs/nature04485.html link]

: News and Views: DeWeese and Zador, "Neurobiology: Efficiency measures" [http://www.nature.com/nature/journal/v439/n7079/full/439920a.html link]

* Olshausen BA, Field DJ. Sparse Coding of Sensory Inputs. Curr Op in Neurobiology, 14: 481-487 (2004). [http://redwood.psych.cornell.edu/papers/current-opinion.pdf link]

* Coding and computation with neural spike trains. W Bialek & A Zee, J. Stat. Phys. 59, 103–115 (1990). [http://www.princeton.edu/~wbialek/our_papers/bialek+zee_90.pdf link]

* Selection from: Spikes: Exploring the Neural Code. F Rieke, D Warland, R de Ruyter van Steveninck & W Bialek (MIT Press, Cambridge, 1997). [http://melvyl.cdlib.org/F/?func=find-b&base=CDL90&request=0-262-18174-6&find_code=020 link]

* <big>✔</big> H. Nover, C. H. Anderson, and G. C. DeAngelis, A Logarithmic, Scale-Invariant Representation of Speed in Macaque Middle Temporal Area Accounts for Speed Discrimination Performance, J. Neurosci., Oct 2005; 25: 10049 - 10060 [http://www.jneurosci.org/cgi/reprint/25/43/10049 link]

* <big>✔</big> Ray Singh and Chris Eliasmith, Higher-Dimensional Neurons Explain the Tuning and Dynamics of Working Memory Cells, J. Neurosci. 2006;26 3667-3678 [http://www.jneurosci.org/cgi/reprint/26/14/3667 link]

==== Cortical Microcircuit/Universal Cortical Algorithm ====

* <big>✔</big> Vernon Mountcastle (1978), "An Organizing Principle for Cerebral Function: The Unit Model and the Distributed System", The Mindful Brain (Gerald M. Edelman and Vernon B. Mountcastle, eds.) Cambridge, MA: MIT Press [http://redwood.berkeley.edu/~amir/pdf/Mountcastle78.pdf link]

* <big>✔</big> Rodney J. Douglas, Kevan A.C. Martin, Neural Circuits of the Neocortex, Annual Review of Neuroscience 2004 27, 419-451 [http://arjournals.annualreviews.org/doi/full/10.1146/annurev.neuro.27.070203.144152 link]

* <big>✔</big> Selection from: Hawkins, J., On Intelligence (Chapter 6) [http://redwood.berkeley.edu/~amir/pdf/HawkinsChap6.pdf link]

* <big>✔</big> Henry Markram, The Blue Brain Project, Nature Neuroscience, 7:153-160, 2006. [http://www.nature.com/nrn/journal/v7/n2/pdf/nrn1848.pdf link]

* Douglas RJ, Martin KAC Whitteridge D. (1989) A canonical microcircuit for neocortex. Neural Computation 1: 480-488. [http://scholar.google.com/scholar?hl=en&lr=&q=douglas+martin+whitteridge+neural+comput+1989&btnG=Search link] (the [http://www.archive.org/details/redwood_center_inaugural_symposium_08 related talk] at the Redwood symposium)

* Cross-modal plasticity in cortical development: differentiation and specification sensory cortex, by Mriganka Sur, Sarah L. Pallas and Anna W. Roe. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=1694329&dopt=Citation link]

* Poggio, T. and E. Bizzi. Generalization in Vision and Motor Control, Nature, Vol. 431, 768-774, 2004. [http://www.nature.com/nature/journal/v431/n7010/abs/nature03014.html link]

* Marr D, "A Theory for Cerebral Neocortex", Proc Roy Soc London(B), 176, 161-234, 1970. [http://adsabs.harvard.edu/abs/1970RSPSB.176..161M link]

==== Feedback, Hierarchical Organization, Generative Models ====

* <big>✔</big> David Mumford, Neuronal Architectures for Pattern-theoretic Problems, in "Large-Scale Neuronal Theories of the Brain", C.Koch & J.Davis, editors, MIT Press, 1994, pp.125-152. [http://redwood.berkeley.edu/~amir/pdf/Neuronal_Mumford.pdf link]

* <big>✔</big> RPN Rao. Bayesian inference and attention in the visual cortex. Neuroreport 16(16), 1843-1848, 2005. [http://www.cs.washington.edu/homes/rao/nreport_bayes_atten05.pdf link]

* TS Lee, D Mumford Hierarchical Bayesian inference in the visual cortex, Journal of the Optical Society of America A, 2003 [http://scholar.google.com/url?sa=U&q=http://www.dam.brown.edu/people/mumford/Papers/JOSALeeMumford.pdf link]

* Felleman, DJ and Van Essen, DC (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex, 1, 1-47. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=1822724&dopt=Abstract link]

==== Manifold Learning ====

* <big>✔</big> Richard Durbin & Graeme Mitchison, A dimension reduction framework for understanding cortical maps, Nature 343, 644 - 647 (15 February 1990) [http://www.nature.com/nature/journal/v343/n6259/abs/343644a0.html link]

* <big>✔</big> Tal Kenet, Dmitri Bibitchkov, Misha Tsodyks, Amiram Grinvald and Amos Arieli, Spontaneously emerging cortical representations of visual attributes, Nature 425, 954-956 (30 October 2003) [http://www.nature.com/nature/journal/v425/n6961/full/nature02078.html link]

* Selection from: Self-Organizing and Associative Memory, T Kohonen - Japanese translation 2nd Edition, Splinger-Verlag Tokyo, 1994 [http://citeseer.ist.psu.edu/context/2930/0 link]

* JB Tenenbaum, V de Silva, JC Langford, A Global Geometric Framework for Nonlinear Dimensionality Reduction, Science, 2000 [http://www.sciencemag.org/cgi/content/full/290/5500/2319 link]

* ST Roweis, LK Saul, Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science, 2000 [http://www.sciencemag.org/cgi/content/full/290/5500/2323 link]

* Nicholas V. Swindale, Doron Shoham, Amiram Grinvald, Tobias Bonhoeffer & Mark Hübener, Visual cortex maps are optimized for uniform coverage, Nature Neuroscience 3, 822 - 826 (2000) [http://www.nature.com/neuro/journal/v3/n8/full/nn0800_822.html link]

Background reading on SOMs
* Neural Computation and Self-Organizing Maps - An Introduction, by Helge Ritter, Thomas Martinetz, and Klaus Schulten, Addison-Wesley, New York, 1992, [http://www.ks.uiuc.edu/Services/Class/PHYS498TBP/spring2002/neuro_4.pdf Kohonen's Network Model] [http://www.ks.uiuc.edu/Services/Class/PHYS498TBP/spring2002/neuro_book.html Contents]

==== Plasticity, Hebbian Learning ====

* Dan, Y. and Poo, M.-m. (2004). Spike timing-dependent plasticity of neural circuits. Neuron 44, 23-30 [http://www.ling.sinica.edu.tw/paper-Mu-ming%2520Poo-2.pdf link]

* Abbott LF, Nelson SB. (2000) Synaptic plasticity: taming the beast. Nat Neurosci. 3 Suppl:1178-83. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=11127835&dopt=Abstract link]

* P. Foldiak, Forming sparse representations by local anti-Hebbian learning, Biological Cybernetics, vol. 64, pp. 165-170, 1990. [http://www.st-andrews.ac.uk/~pf2/FoldiakSparseBC90.pdf link]

* M. Tsodyks, Spike-timing-dependent synaptic plasticity–The long road towards understanding neuronal mechanisms Trends in Neuroscience, 2002. [http://dx.doi.org/10.1016/S0166-2236(02)02294-4 link]

* Saudargienne A, Porr B, and Worgotter F. How the shape of pre- and postsynaptic signals can influence STDP: a biophysical model. Neural Comp 16: 595–625, 2004. [http://neco.mitpress.org/cgi/content/abstract/16/3/595 link]

* Seung, HS (2000) Half a century of Hebb. Nat. Neurosc. Suppl: 1166. [http://www.nature.com/cgi-taf/DynaPage.taf?file=/neuro/journal/v3/n11s/full/nn1100_1166.html link]

* H. S. Seung. Learning in spiking neural networks by reinforcement of stochastic synaptic transmission. Neuron 40, 1063-1073 (2003). [http://hebb.mit.edu/people/seung/papers/Neuron18Dec03.pdf link]

* Hinton, G. E. and Sejnowski, T. J. (1986), Learning and relearning in Boltzmann machines. In Rumelhart, D. E. and McClelland, J. L., editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations, MIT Press, Cambridge, MA. [http://portal.acm.org/citation.cfm?coll=GUIDE&dl=GUIDE&id=104291 link]

==== Oscillations ====

* Gray, CM (1999) The temporal correlation hypothesis of visual feature integration: still alive and well. Neuron. 1999, 24(1):31-47, 111-25. Review. [http://scholar.google.com/url?sa=U&q=http://lifesci.rutgers.edu/~auerbach/Gray%2520binding.pdf link]

* Neuron Special Issue on Oscillations, 1999, 24(1) [http://www.sciencedirect.com/science?_ob=IssueURL&_tockey=%23TOC%237054%231999%23999759998%23575347%23FLA%23&_auth=y&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=fcf1d593bba077aaec2c4f34adbe071d link]

==== Associative Memory ====

* <big>✔</big> J Hopfield. Neural Networks and Physical Systems with Emergent Collective Computational Abilities. PNAS, 79:2554-2558 (1982) [http://www.pnas.org/cgi/content/abstract/79/8/2554 link]
** David MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge Univ. Press, 2003. [http://www.inference.phy.cam.ac.uk/mackay/itprnn/ps/504.520.pdf Chapter 42 (Hopfield networks)]

* <big>✔</big> S. Fusi, P.J. Drew, and L.F. Abbott, Cascade Models of Synaptically Stored Memories, Neuron 2005 45: 599-611. [http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B6WSS-4FH5K0X-H-1&_cdi=7054&_user=4420&_orig=search&_coverDate=02%2F17%2F2005&_qd=1&_sk=999549995&view=c&wchp=dGLbVlz-zSkWz&md5=1b45b90c5fb16f945ba490ba48503de7&ie=/sdarticle.pdf link]

* G. Palm. On the storage capacity of an associative memory with randomly distributed storage elements. Biol. Cybernetics. 36:19-31 (1980). [http://www.springerlink.com/link.asp?id=wp7q375127415311 link]

* Selection from: Self-Organizing and Associative Memory, T Kohonen - Japanese translation 2nd Edition, Splinger-Verlag Tokyo, 1994

==== Models of Invariance ====

* Olshausen BA, Anderson CH, Van Essen DC (1993). A Neurobiological Model of Visual Attention and Invariant Pattern Recognition Based on Dynamic Routing of Information, The Journal of Neuroscience, 13(11), 4700-4719. [http://redwood.berkeley.edu/bruno/papers/ link]

* K Fukushima, Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biological Cybernetics (Historical Archive), Volume 36, Issue 4, Apr 1980, Pages 193 - 202 [http://www.springerlink.com/link.asp?id=r6g5w3tt54528137 link]

* <big>✔</big> P. Foldiak, Learning invariance from transformation sequences, Neural Computation, vol. 3, pp. 194-200, 1991. [http://www.st-andrews.ac.uk/~pf2/FoldiakInvarianceLearningNC91.pdf link]

* <big>✔</big> L Wiskott. Slow Feature Analysis: Unsupervised Learning of Invariances. Neural Computation. 2002;14:715-770. [http://redwood.berkeley.edu/~cadieu/pdf/WiskottSejnowski-SlowFeature.pdf link]

==== Active Perception-sensorimotor loops ====

* Churchland P, Ramachandran VS, Sejnowski TJ. Large-Scale Neuronal Theories of the Brain: Chapter 2, Critique of Pure Vision, 1994. [http://cnl.salk.edu/~terry/PDF/PureVision.94.pdf link]

* Oregan JK, Noe A. A sensorimotor account of vision and visual consciousness, BBS (2001) 24:939-1031. [http://ist-socrates.berkeley.edu/~noe/oregan.noe.pdf link]

* Philipona D, O'Regan JK, Nadal JP. Is There Something Out There? Inferring Space from Sensorimotor Dependencies. Neural Computation 2003;15:2029-2049. [http://neco.mitpress.org/cgi/content/abstract/15/9/2029 link]

====Birdsong====

* Anthony Leonardo & Michale S. Fee. Ensemble Coding of Vocal Control in Birdsong. The Journal of Neuroscience, January 19, 2005, 25(3):652-661. [http://www.jneurosci.org/cgi/reprint/25/3/652 link (Warning: 27.7 MB .pdf)]

* Troyer TW, Bottjer SW. Birdsong: models and mechanisms. Curr Opin Neurobiol. 2001 Dec;11(6):721-6. [http://redwood.berkeley.edu/~cadieu/pdf/TroyerBottjer_Birdsong_CurrOpinNeuro01.pdf link]

==== Theories of the Ventral Stream ====

* <big>✔</big> Ullman, S. (1995). Sequence-seeking and counter streams: A computational model for bi-directional information flow in the visual cortex. Cerebral Cortex, 5(1) 1-11. [http://redwood.berkeley.edu/~cadieu/pdf/UllmanCerCor95.pdf link]

* <big>✔</big> Riesenhuber, M., and T. Poggio. Models of Object Recognition, Nature Neuroscience, 3 Supp., 1199-1204, 2000. [http://www.nature.com/neuro/journal/v3/n11s/pdf/nn1100_1199.pdf link]

* Riesenhuber, M. and T. Poggio. How Visual Cortex Recognizes Objects: The Tale of the Standard Model. In: The Visual Neurosciences, (Eds. L.M. Chalupa and J.S. Werner), MIT Press, Cambridge, MA, Vol. 2, 1640-1653, 2003. [http://cbcl.mit.edu/projects/cbcl/publications/ps/max-vis-cortex-03.pdf link]

* Rolls,E.T. (1997) A neurophysiological and computational approach to the functions of the temporal lobe cortical visual areas in invariant object recognition. Chapter 9, pp. 184-220 in Computational and Psychophysical Mechanisms of Visual Coding, eds. M.Jenkin and L.Harris. Cambridge University Press: Cambridge. [http://www.cns.ox.ac.uk/publications.html link]

====Sleep====
* <big>✔</big> Crick, Francis, and Graeme Mitchison. "The Function of Dream Sleep." Nature 304, (14 July 1983): 111-114. [http://redwood.berkeley.edu/~cadieu/pdf/CrickMitchisonDreamSleep1983.pdf link]

* <big>✔</big> Sutherland GR, McNaughton B., Memory trace reactivation in hippocampal and neocortical neuronal ensembles., Curr Opin Neurobiol. 2000 Apr;10(2):180-6 [http://redwood.berkeley.edu/~cadieu/pdf/SutherlandMcNaughtonMemoryReactivation1999.pdf link]

==== Theories of Hippocampus ====

* Becker, S. (2005) "A computational principle for hippocampal learning and neurogenesis". Hippocampus 15(6):722-738. [http://www.science.mcmaster.ca/Psychology/sb.html link]

* Leutgeb, S., Leutgeb, J.K., Moser, M.-B., and Moser, E.I. (2005). Place cells, spatial maps and the population code for memory. Current Opinion in Neurobiology, 15, 738-746. [http://www.cbm.ntnu.no/publications link]

* [http://www.bris.ac.uk/synaptic/research/projects/memory/spatialmem.htm place cells]

* Hasselmo, M.E. and McClelland, J.L. (1999) Neural models of memory. Curr. Opinion Neurobiol. 9: 184-188. [http://people.bu.edu/hasselmo/HasselmoMcClelland.pdf link]

==== Motor System ====

* Körding, KP. and Wolpert, D. (2004) Bayesian Integration in Sensorimotor Learning, Nature 427:244-247. [http://www.koerding.com/pubs/koerdingNature2004.pdf link]

* Todorov E Direct cortical control of muscle activation in voluntary arm movements: a model. (2000) Nature Neuroscience 3(4): 391-398 [http://cogsci.ucsd.edu/~todorov/papers/mi.pdf link]

* Todorov E. On the role of primary motor cortex in arm movement control (2003) In Progress in Motor Control III, ch 6, pp 125-166, Latash and Levin (eds), Human Kinetics [http://cogsci.ucsd.edu/~todorov/papers/MotorCortex.pdf link]

* Georgopoulos AP. Neuron. 1994 Aug;13(2):257-68. New concepts in generation of movement. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=8060612&dopt=Abstract link]

* Scott, S.H. (2004) Optimal feedback control and the neural basis of volitional motor control. Nature Reviews Neuroscience 5:532-546. [http://limb.biomed.queensu.ca/publications/optimal_feedback_control_and_the_neural_basis.pdf link]

Suggestion Board

2006-04-06T16:40:48Z

TCN: Added "Ensemble Coding of Vocal Control in Birdsong."

Link to Mainpage: [[TCN]]

== Post Your Suggested Topics or Paper (click on Edit)==

===Sleep===
* Crick, Francis, and Graeme Mitchison. "The Function of Dream Sleep." Nature 304, (14 July 1983): 111-114.

* David J. Foster and Matthew A. Wilson, Reverse replay of behavioural sequences in hippocampal place cells during the awake state, Nature 440, 680-683 (30 March 2006) [http://www.nature.com/nature/journal/v440/n7084/pdf/nature04587.pdf link]

===Other topics===

*Karklin, Lewicki. A Hierarchical Bayesian Model for Learning Nonlinear Statistical Regularities in Nonstationary Natural Signals. Neural Computation. 2005;17:397-423. [http://video.google.com/videoplay?docid=-5115453018099427284&q=msri MSRI talk]

*Guetig, Sompolinsky. The Tempotron: a neuron that learns spike timing-based decisions. Nature Neuroscience 2006; 9:420-428. [http://www.nature.com/neuro/journal/v9/n3/pdf/nn1643.pdf]

*Hinton, Dayan, Frey, Neal. The wake-sleep algorithm for unsupervised Neural Networks. Science, 268, 1158-1161.

*Nakayama, He, Shimojo. Visual Surface Representation: A critical link between Lower-level and Higher-level vision. Visual Congnition, 1995. [http://redwood.berkeley.edu/~amir/pdf/nakayama-1995.pdf]

* Zhu, Wu, Mumford. Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling. International Journal of Computer Vision 27(2), 107-126 (1998). [http://www.stat.ucla.edu/~sczhu/papers/FRAME.pdf]

* George, Hawkins. A Hierarchical Bayesian Model of Invariant Pattern Recognition in the Visual Cortex. [http://www.stanford.edu/~dil/invariance/Download/GeorgeHawkinsIJCNN05.pdf link]

* D.P. Wipf and B.D. Rao, "Sparse Bayesian Learning for Basis Selection," IEEE Transactions on Signal Processing, vol. 52, no. 8, August 2004. [http://ieeexplore.ieee.org/xpl/tocresult.jsp?isNumber=29166&puNumber=78 link]

* Alan A Stocker & Eero P Simoncelli. Noise characteristics and prior expectations in human visual speed perception. Nature Neuroscience 9, 578 - 585 (2006) [http://www.nature.com/neuro/journal/v9/n4/abs/nn1669.html]

* Wilson RI, Turner GC, and Laurent G. Transformation of olfactory representations in the Drosophila antennal lobe Science 303: 366-370 (2004) [http://marvin.caltech.edu/PDF/WilsonetalScience2004.pdf]

===Computational Biology===
* Paul W. K. Rothemund. Folding DNA to create nanoscale shapes and patterns. Nature 440, 297-302 (16 March 2006). [http://www.nature.com/nature/journal/v440/n7082/abs/nature04586.html link]

===Birdsong===

* Anthony Leonardo & Michale S. Fee. Ensemble Coding of Vocal Control in Birdsong. The Journal of Neuroscience, January 19, 2005, 25(3):652-661. [http://www.jneurosci.org/cgi/reprint/25/3/652 link (Warning: 27.7 MB .pdf)]

Here's an example for formating:

===Topic===
* E. Smith and M. S. Lewicki, Efficient Auditory Coding, Nature, 439 (7079), 2006. [http://www.nature.com/nature/journal/v439/n7079/abs/nature04485.html link]

: News and Views: DeWeese and Zador, "Neurobiology: Efficiency measures". [http://www.nature.com/nature/journal/v439/n7079/full/439920a.html link]

Suggestion Board

2006-03-15T21:00:04Z

TCN: /* Other topics */

Suggestion Board

2006-03-15T20:48:22Z

TCN: /* Sleep */

Suggestion Board

2006-03-15T20:46:43Z

TCN: /* Sleep */

Suggestion Board

2006-03-09T00:45:17Z

TCN: /* Post Your Suggested Topics or Paper (click on Edit) */

[[TCN]]

== Post Your Suggested Topics or Paper (click on Edit)==

===Sleep===
* Crick, Francis, and Graeme Mitchison. "The Function of Dream Sleep." Nature 304, (14 July 1983): 111-114.
* David J. Foster and Matthew A. Wilson, Reverse replay of behavioural sequences in hippocampal place cells during the awake state, Nature advance online publication; published online 12 February 2006

Here's an example for formating:
===Topic===
* E. Smith and M. S. Lewicki, Efficient Auditory Coding, Nature, 439 (7079), 2006. [http://www.nature.com/nature/journal/v439/n7079/abs/nature04485.html link]

: News and Views: DeWeese and Zador, "Neurobiology: Efficiency measures". [http://www.nature.com/nature/journal/v439/n7079/full/439920a.html link]

Suggestion Board

2006-03-09T00:44:52Z

TCN: /* Post Your Suggested Topics or Paper (click on Edit) */

== Post Your Suggested Topics or Paper (click on Edit)==

===Sleep===
* Crick, Francis, and Graeme Mitchison. "The Function of Dream Sleep." Nature 304, (14 July 1983): 111-114.
* David J. Foster and Matthew A. Wilson, Reverse replay of behavioural sequences in hippocampal place cells during the awake state, Nature advance online publication; published online 12 February 2006

Here's an example for formating:
===Topic===
* E. Smith and M. S. Lewicki, Efficient Auditory Coding, Nature, 439 (7079), 2006. [http://www.nature.com/nature/journal/v439/n7079/abs/nature04485.html link]

: News and Views: DeWeese and Zador, "Neurobiology: Efficiency measures". [http://www.nature.com/nature/journal/v439/n7079/full/439920a.html link]

Suggestion Board

2006-03-09T00:44:32Z

TCN: /* Post Your Suggested Topics or Paper (click on Edit) */

== Post Your Suggested Topics or Paper (click on Edit)==

* Crick, Francis, and Graeme Mitchison. "The Function of Dream Sleep." Nature 304, (14 July 1983): 111-114.
* David J. Foster and Matthew A. Wilson, Reverse replay of behavioural sequences in hippocampal place cells during the awake state, Nature advance online publication; published online 12 February 2006

Here's an example for formating:
===Topic===
* E. Smith and M. S. Lewicki, Efficient Auditory Coding, Nature, 439 (7079), 2006. [http://www.nature.com/nature/journal/v439/n7079/abs/nature04485.html link]

: News and Views: DeWeese and Zador, "Neurobiology: Efficiency measures". [http://www.nature.com/nature/journal/v439/n7079/full/439920a.html link]

TCN

2006-03-07T03:02:19Z

TCN: /* Make a Topic or Paper Suggestion */

== Topics in Computational Neuroscience ==

=== Overview ===
'''UPDATE:''' If you would like, please register for VS 298 Section 3, Course Control Number (CCN) 66487, for 1 unit, S/U. The class will later be cross listed under Neuroscience.

'''NOTE:''' ''This semester (Spring '06) we are having a trial run of the class, Topics in Computational Neuroscience. We hope that this will be an ongoing class that will cover new papers and topics each semester. This semester we are planning to cover a topic each week and choose about two papers for each topic. Topics and papers we skip this semester will be covered in future semesters.''

This class is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. The class will provide a broad survey of literature from theoretical and computational neuroscience. Readings for the class will be selected from the best papers in the field and will combine both seminal works and recent theories. The class is scheduled to meet for one session each week for 1.5 hours for each session.

=== E-mail List ===

redwood_tcn at lists.berkeley.edu

You can subscribe yourself via the web [http://list.berkeley.edu link] or by sending mail to: majordomo@lists.berkeley.edu that contains:

subscribe redwood_tcn

in the body of the message.

=== Guidelines for Presenting Papers ===
Each person that selects a paper should present, in about 10 minutes (no slides):
* an executive summary
* an outline of the key points, ideas, or contributions
* a description of the key figures
* what you took away from the paper
* some potential questions for discussion

=== Make a Topic or Paper Suggestion ===

[[Suggestion Board]] (To gain access send email to: cadieu at berkeley dot edu)

=== Readings for Next Meeting! (March 8th) ===

* E. Smith and M. S. Lewicki, Efficient Auditory Coding, Nature, 439 (7079), 2006. [http://www.nature.com/nature/journal/v439/n7079/abs/nature04485.html link]

: News and Views: DeWeese and Zador, "Neurobiology: Efficiency measures". [http://www.nature.com/nature/journal/v439/n7079/full/439920a.html link]

* D. Y. Tsao, W. A. Freiwald, R. B. H. Tootell, M. S. Livingstone, A Cortical Region Consisting Entirely of Face-Selective Cells, Science 3 February 2006; Vol. 311. no. 5761, pp. 670 - 674. [http://www.sciencemag.org/cgi/content/full/311/5761/670 link]

: Perspectives: Kanwisher, "What's in a Face?". [http://www.sciencemag.org/cgi/content/full/311/5761/617 link]

=== Time and Location ===
7:00 PM on Wednesdays in the Beach room, 3105 Tolman (the doors on the east side of the building should be open).

=== Syllabus (Topics and Readings) ===

<big>✔</big> indicates papers we've read.

==== Early Work ====

* <big>✔</big> K. A. C. Martin, The Pope and grandmother−a frog's-eye view of theory, Nature Neuroscience 3, 1169 (2000) [http://www.nature.com/neuro/journal/v3/n11s/full/nn1100_1169.html link]

* <big>✔</big> Lettvin, Maturana, McCulloch, Pitts, "What the Frog's Eye Tells the Frog's Brian" [http://jerome.lettvin.info/WhatTheFrogsEyeTellsTheFrogsBrain.pdf link]

* <big>✔</big> Barlow HB (1972) Single Neurons and Sensation: A neuron doctrine for perceptual psychology. Perception 1, 371-394. [http://redwood.berkeley.edu/~amir/pdf/Barlow1972.pdf link]

* Marvin Minsky, Steps Toward Artificial Intelligence [http://portal.acm.org/citation.cfm?id=216408.216442&coll=GUIDE&dl=GUIDE link]

* McCulloch and Pitts, “A logical calculus of the ideas immanent in nervous activity” (1943) [http://portal.acm.org/citation.cfm?coll=GUIDE&dl=GUIDE&id=104377 link]

* Marr, selection from Vision; Artificial Intelligence—a personal view, by David Marr [http://scholar.google.com/url?sa=U&q=https://dspace.mit.edu/handle/1721.1/5776%3Fmode%3Dsimple link]

* Readings from Dartmouth Conf. 1956 proceedings
* Rosenblatt, Frank (1962). Principles of neurodynamics. New York: Spartan. Cf. Rumelhart, D.E., J. L. McClelland and the PDP Research Group (1986). Parallel Distributed Processing vol. 1&2. Cambridge: MIT. [http://scholar.google.com/scholar?hl=en&lr=&q=rosenblatt+principles+of+neurodynamics&btnG=Search link]

* "Making a Mind Versus Modeling the Brain: Artificial Intelligence Back at a Branchpoint" (with H. Dreyfus),Daedulus, Winter 1988 [http://portal.acm.org/citation.cfm?id=63323.66521 link]

==== Coding ====

* <big>✔</big> Kreiman, G. Neural Coding: Computational and Biophysical Perspectives, Physics of Life Reviews, 2, 71-102, 2004. [http://dx.doi.org/10.1016/j.plrev.2004.06.001 link]

* <big>✔</big> Pouget, A, Dayan, P & Zemel, RS (2000). Information processing with population codes. Nature Reviews Neuroscience, 1 , 125-132. [http://www.nature.com/nrn/journal/v1/n2/full/nrn1100_125a_fs.html link]

* E. Smith and M. S. Lewicki, Efficient Auditory Coding, Nature, 439 (7079), 2006. [http://www.nature.com/nature/journal/v439/n7079/abs/nature04485.html link]

: News and Views: DeWeese and Zador, "Neurobiology: Efficiency measures" [http://www.nature.com/nature/journal/v439/n7079/full/439920a.html link]

* Olshausen BA, Field DJ. Sparse Coding of Sensory Inputs. Curr Op in Neurobiology, 14: 481-487 (2004). [http://redwood.psych.cornell.edu/papers/current-opinion.pdf link]

* Coding and computation with neural spike trains. W Bialek & A Zee, J. Stat. Phys. 59, 103–115 (1990). [http://www.princeton.edu/~wbialek/our_papers/bialek+zee_90.pdf link]

* Selection from: Spikes: Exploring the Neural Code. F Rieke, D Warland, R de Ruyter van Steveninck & W Bialek (MIT Press, Cambridge, 1997). [http://melvyl.cdlib.org/F/?func=find-b&base=CDL90&request=0-262-18174-6&find_code=020 link]

==== Cortical Microcircuit/Universal Cortical Algorithm ====

* <big>✔</big> Vernon Mountcastle (1978), "An Organizing Principle for Cerebral Function: The Unit Model and the Distributed System", The Mindful Brain (Gerald M. Edelman and Vernon B. Mountcastle, eds.) Cambridge, MA: MIT Press [http://redwood.berkeley.edu/~amir/pdf/Mountcastle78.pdf link]

* <big>✔</big> Rodney J. Douglas, Kevan A.C. Martin, Neural Circuits of the Neocortex, Annual Review of Neuroscience 2004 27, 419-451 [http://arjournals.annualreviews.org/doi/full/10.1146/annurev.neuro.27.070203.144152 link]

* <big>✔</big> Selection from: Hawkins, J., On Intelligence (Chapter 6) [http://redwood.berkeley.edu/~amir/pdf/HawkinsChap6.pdf link]

* <big>✔</big> Henry Markram, The Blue Brain Project, Nature Neuroscience, 7:153-160, 2006. [http://www.nature.com/nrn/journal/v7/n2/pdf/nrn1848.pdf link]

* Douglas RJ, Martin KAC Whitteridge D. (1989) A canonical microcircuit for neocortex. Neural Computation 1: 480-488. [http://scholar.google.com/scholar?hl=en&lr=&q=douglas+martin+whitteridge+neural+comput+1989&btnG=Search link] (the [http://www.archive.org/details/redwood_center_inaugural_symposium_08 related talk] at the Redwood symposium)

* Cross-modal plasticity in cortical development: differentiation and specification sensory cortex, by Mriganka Sur, Sarah L. Pallas and Anna W. Roe. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=1694329&dopt=Citation link]

* Poggio, T. and E. Bizzi. Generalization in Vision and Motor Control, Nature, Vol. 431, 768-774, 2004. [http://www.nature.com/nature/journal/v431/n7010/abs/nature03014.html link]

* Marr D, "A Theory for Cerebral Neocortex", Proc Roy Soc London(B), 176, 161-234, 1970. [http://adsabs.harvard.edu/abs/1970RSPSB.176..161M link]

==== Feedback, Hierarchical Organization, Generative Models ====

* <big>✔</big> David Mumford, Neuronal Architectures for Pattern-theoretic Problems, in "Large-Scale Neuronal Theories of the Brain", C.Koch & J.Davis, editors, MIT Press, 1994, pp.125-152. [http://redwood.berkeley.edu/~amir/pdf/Neuronal_Mumford.pdf link]

* <big>✔</big> RPN Rao. Bayesian inference and attention in the visual cortex. Neuroreport 16(16), 1843-1848, 2005. [http://www.cs.washington.edu/homes/rao/nreport_bayes_atten05.pdf link]

* TS Lee, D Mumford Hierarchical Bayesian inference in the visual cortex, Journal of the Optical Society of America A, 2003 [http://scholar.google.com/url?sa=U&q=http://www.dam.brown.edu/people/mumford/Papers/JOSALeeMumford.pdf link]

* Felleman, DJ and Van Essen, DC (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex, 1, 1-47. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=1822724&dopt=Abstract link]

==== Manifold Learning ====

* <big>✔</big> Richard Durbin & Graeme Mitchison, A dimension reduction framework for understanding cortical maps, Nature 343, 644 - 647 (15 February 1990) [http://www.nature.com/nature/journal/v343/n6259/abs/343644a0.html link]

* <big>✔</big> Tal Kenet, Dmitri Bibitchkov, Misha Tsodyks, Amiram Grinvald and Amos Arieli, Spontaneously emerging cortical representations of visual attributes, Nature 425, 954-956 (30 October 2003) [http://www.nature.com/nature/journal/v425/n6961/full/nature02078.html link]

* Selection from: Self-Organizing and Associative Memory, T Kohonen - Japanese translation 2nd Edition, Splinger-Verlag Tokyo, 1994 [http://citeseer.ist.psu.edu/context/2930/0 link]

* JB Tenenbaum, V de Silva, JC Langford, A Global Geometric Framework for Nonlinear Dimensionality Reduction, Science, 2000 [http://www.sciencemag.org/cgi/content/full/290/5500/2319 link]

* ST Roweis, LK Saul, Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science, 2000 [http://www.sciencemag.org/cgi/content/full/290/5500/2323 link]

* Nicholas V. Swindale, Doron Shoham, Amiram Grinvald, Tobias Bonhoeffer & Mark Hübener, Visual cortex maps are optimized for uniform coverage, Nature Neuroscience 3, 822 - 826 (2000) [http://www.nature.com/neuro/journal/v3/n8/full/nn0800_822.html link]

Background reading on SOMs
* Neural Computation and Self-Organizing Maps - An Introduction, by Helge Ritter, Thomas Martinetz, and Klaus Schulten, Addison-Wesley, New York, 1992, [http://www.ks.uiuc.edu/Services/Class/PHYS498TBP/spring2002/neuro_4.pdf Kohonen's Network Model] [http://www.ks.uiuc.edu/Services/Class/PHYS498TBP/spring2002/neuro_book.html Contents]

==== Plasticity, Hebbian Learning ====

* Dan, Y. and Poo, M.-m. (2004). Spike timing-dependent plasticity of neural circuits. Neuron 44, 23-30 [http://www.ling.sinica.edu.tw/paper-Mu-ming%2520Poo-2.pdf link]

* Abbott LF, Nelson SB. (2000) Synaptic plasticity: taming the beast. Nat Neurosci. 3 Suppl:1178-83. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=11127835&dopt=Abstract link]

* P. Foldiak, Forming sparse representations by local anti-Hebbian learning, Biological Cybernetics, vol. 64, pp. 165-170, 1990. [http://www.st-andrews.ac.uk/~pf2/FoldiakSparseBC90.pdf link]

* M. Tsodyks, Spike-timing-dependent synaptic plasticity–The long road towards understanding neuronal mechanisms Trends in Neuroscience, 2002. [http://dx.doi.org/10.1016/S0166-2236(02)02294-4 link]

* Saudargienne A, Porr B, and Worgotter F. How the shape of pre- and postsynaptic signals can influence STDP: a biophysical model. Neural Comp 16: 595–625, 2004. [http://neco.mitpress.org/cgi/content/abstract/16/3/595 link]

* Seung, HS (2000) Half a century of Hebb. Nat. Neurosc. Suppl: 1166. [http://www.nature.com/cgi-taf/DynaPage.taf?file=/neuro/journal/v3/n11s/full/nn1100_1166.html link]

* H. S. Seung. Learning in spiking neural networks by reinforcement of stochastic synaptic transmission. Neuron 40, 1063-1073 (2003). [http://hebb.mit.edu/people/seung/papers/Neuron18Dec03.pdf link]

* Hinton, G. E. and Sejnowski, T. J. (1986), Learning and relearning in Boltzmann machines. In Rumelhart, D. E. and McClelland, J. L., editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations, MIT Press, Cambridge, MA. [http://portal.acm.org/citation.cfm?coll=GUIDE&dl=GUIDE&id=104291 link]

==== Oscillations ====

* Gray, CM (1999) The temporal correlation hypothesis of visual feature integration: still alive and well. Neuron. 1999, 24(1):31-47, 111-25. Review. [http://scholar.google.com/url?sa=U&q=http://lifesci.rutgers.edu/~auerbach/Gray%2520binding.pdf link]

* Neuron Special Issue on Oscillations, 1999, 24(1) [http://www.sciencedirect.com/science?_ob=IssueURL&_tockey=%23TOC%237054%231999%23999759998%23575347%23FLA%23&_auth=y&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=fcf1d593bba077aaec2c4f34adbe071d link]

==== Associative Memory ====

* J Hopfield. Neural Networks and Physical Systems with Emergent Collective Computational Abilities. PNAS, 79:2554-2558 (1982) [http://www.pnas.org/cgi/content/abstract/79/8/2554 link]

* G. Palm. On the storage capacity of an associative memory with randomly distributed storage elements. Biol. Cybernetics. 36:19-31 (1980). [http://www.springerlink.com/link.asp?id=wp7q375127415311 link]

* Selection from: Self-Organizing and Associative Memory, T Kohonen - Japanese translation 2nd Edition, Splinger-Verlag Tokyo, 1994

==== Models of Invariance ====

* Olshausen BA, Anderson CH, Van Essen DC (1993). A Neurobiological Model of Visual Attention and Invariant Pattern Recognition Based on Dynamic Routing of Information, The Journal of Neuroscience, 13(11), 4700-4719. [http://redwood.berkeley.edu/bruno/papers/ link]

* K Fukushima, Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biological Cybernetics (Historical Archive), Volume 36, Issue 4, Apr 1980, Pages 193 - 202 [http://www.springerlink.com/link.asp?id=r6g5w3tt54528137 link]

* P. Foldiak, Learning invariance from transformation sequences, Neural Computation, vol. 3, pp. 194-200, 1991. [http://www.st-andrews.ac.uk/~pf2/FoldiakInvarianceLearningNC91.pdf link]

* L Wiskott. Slow Feature Analysis: Unsupervised Learning of Invariances. Neural Computation. 2002;14:715-770. [http://neco.mitpress.org/cgi/content/abstract/14/4/715 link]

==== Active Perception-sensorimotor loops ====

* Churchland P, Ramachandran VS, Sejnowski TJ. Large-Scale Neuronal Theories of the Brain: Chapter 2, Critique of Pure Vision, 1994. [http://cnl.salk.edu/~terry/PDF/PureVision.94.pdf link]

* Oregan JK, Noe A. A sensorimotor account of vision and visual consciousness, BBS (2001) 24:939-1031. [http://ist-socrates.berkeley.edu/~noe/oregan.noe.pdf link]

* Philipona D, O'Regan JK, Nadal JP. Is There Something Out There? Inferring Space from Sensorimotor Dependencies. Neural Computation 2003;15:2029-2049. [http://neco.mitpress.org/cgi/content/abstract/15/9/2029 link]

==== Theories of the Ventral Stream ====

* Ullman "Streams and Counter Streams", chapter in Large Scale Neuronal Theories of the Brain
* Riesenhuber, M. and T. Poggio. How Visual Cortex Recognizes Objects: The Tale of the Standard Model. In: The Visual Neurosciences, (Eds. L.M. Chalupa and J.S. Werner), MIT Press, Cambridge, MA, Vol. 2, 1640-1653, 2003. [http://cbcl.mit.edu/projects/cbcl/publications/ps/max-vis-cortex-03.pdf link]

* Rolls,E.T. (1997) A neurophysiological and computational approach to the functions of the temporal lobe cortical visual areas in invariant object recognition. Chapter 9, pp. 184-220 in Computational and Psychophysical Mechanisms of Visual Coding, eds. M.Jenkin and L.Harris. Cambridge University Press: Cambridge. [http://www.cns.ox.ac.uk/publications.html link]

==== Theories of Hippocampus ====

* Becker, S. (2005) "A computational principle for hippocampal learning and neurogenesis". Hippocampus 15(6):722-738. [http://www.science.mcmaster.ca/Psychology/sb.html link]

* Leutgeb, S., Leutgeb, J.K., Moser, M.-B., and Moser, E.I. (2005). Place cells, spatial maps and the population code for memory. Current Opinion in Neurobiology, 15, 738-746. [http://www.cbm.ntnu.no/publications link]

* [http://www.bris.ac.uk/synaptic/research/projects/memory/spatialmem.htm place cells]

==== Motor System ====

* Körding, KP. and Wolpert, D. (2004) Bayesian Integration in Sensorimotor Learning, Nature 427:244-247. [http://www.koerding.com/pubs/koerdingNature2004.pdf link]

TCN

2006-03-07T03:02:08Z

TCN: /* Make a Topic or Paper Suggestion */

== Topics in Computational Neuroscience ==

=== Overview ===
'''UPDATE:''' If you would like, please register for VS 298 Section 3, Course Control Number (CCN) 66487, for 1 unit, S/U. The class will later be cross listed under Neuroscience.

'''NOTE:''' ''This semester (Spring '06) we are having a trial run of the class, Topics in Computational Neuroscience. We hope that this will be an ongoing class that will cover new papers and topics each semester. This semester we are planning to cover a topic each week and choose about two papers for each topic. Topics and papers we skip this semester will be covered in future semesters.''

This class is aimed at graduate students from the neuroscience program, neuroscience related life sciences, as well as students from engineering, physics, and math programs with an interest in a computational approach to studying the brain. The class will provide a broad survey of literature from theoretical and computational neuroscience. Readings for the class will be selected from the best papers in the field and will combine both seminal works and recent theories. The class is scheduled to meet for one session each week for 1.5 hours for each session.

=== E-mail List ===

redwood_tcn at lists.berkeley.edu

You can subscribe yourself via the web [http://list.berkeley.edu link] or by sending mail to: majordomo@lists.berkeley.edu that contains:

subscribe redwood_tcn

in the body of the message.

=== Guidelines for Presenting Papers ===
Each person that selects a paper should present, in about 10 minutes (no slides):
* an executive summary
* an outline of the key points, ideas, or contributions
* a description of the key figures
* what you took away from the paper
* some potential questions for discussion

=== Make a Topic or Paper Suggestion ===

[[Suggestion Board]] To gain access send email to: cadieu at berkeley dot edu

=== Readings for Next Meeting! (March 8th) ===

* E. Smith and M. S. Lewicki, Efficient Auditory Coding, Nature, 439 (7079), 2006. [http://www.nature.com/nature/journal/v439/n7079/abs/nature04485.html link]

: News and Views: DeWeese and Zador, "Neurobiology: Efficiency measures". [http://www.nature.com/nature/journal/v439/n7079/full/439920a.html link]

* D. Y. Tsao, W. A. Freiwald, R. B. H. Tootell, M. S. Livingstone, A Cortical Region Consisting Entirely of Face-Selective Cells, Science 3 February 2006; Vol. 311. no. 5761, pp. 670 - 674. [http://www.sciencemag.org/cgi/content/full/311/5761/670 link]

: Perspectives: Kanwisher, "What's in a Face?". [http://www.sciencemag.org/cgi/content/full/311/5761/617 link]

=== Time and Location ===
7:00 PM on Wednesdays in the Beach room, 3105 Tolman (the doors on the east side of the building should be open).

=== Syllabus (Topics and Readings) ===

<big>✔</big> indicates papers we've read.

==== Early Work ====

* <big>✔</big> K. A. C. Martin, The Pope and grandmother−a frog's-eye view of theory, Nature Neuroscience 3, 1169 (2000) [http://www.nature.com/neuro/journal/v3/n11s/full/nn1100_1169.html link]

* <big>✔</big> Lettvin, Maturana, McCulloch, Pitts, "What the Frog's Eye Tells the Frog's Brian" [http://jerome.lettvin.info/WhatTheFrogsEyeTellsTheFrogsBrain.pdf link]

* <big>✔</big> Barlow HB (1972) Single Neurons and Sensation: A neuron doctrine for perceptual psychology. Perception 1, 371-394. [http://redwood.berkeley.edu/~amir/pdf/Barlow1972.pdf link]

* Marvin Minsky, Steps Toward Artificial Intelligence [http://portal.acm.org/citation.cfm?id=216408.216442&coll=GUIDE&dl=GUIDE link]

* McCulloch and Pitts, “A logical calculus of the ideas immanent in nervous activity” (1943) [http://portal.acm.org/citation.cfm?coll=GUIDE&dl=GUIDE&id=104377 link]

* Marr, selection from Vision; Artificial Intelligence—a personal view, by David Marr [http://scholar.google.com/url?sa=U&q=https://dspace.mit.edu/handle/1721.1/5776%3Fmode%3Dsimple link]

* Readings from Dartmouth Conf. 1956 proceedings
* Rosenblatt, Frank (1962). Principles of neurodynamics. New York: Spartan. Cf. Rumelhart, D.E., J. L. McClelland and the PDP Research Group (1986). Parallel Distributed Processing vol. 1&2. Cambridge: MIT. [http://scholar.google.com/scholar?hl=en&lr=&q=rosenblatt+principles+of+neurodynamics&btnG=Search link]

* "Making a Mind Versus Modeling the Brain: Artificial Intelligence Back at a Branchpoint" (with H. Dreyfus),Daedulus, Winter 1988 [http://portal.acm.org/citation.cfm?id=63323.66521 link]

==== Coding ====

* <big>✔</big> Kreiman, G. Neural Coding: Computational and Biophysical Perspectives, Physics of Life Reviews, 2, 71-102, 2004. [http://dx.doi.org/10.1016/j.plrev.2004.06.001 link]

* <big>✔</big> Pouget, A, Dayan, P & Zemel, RS (2000). Information processing with population codes. Nature Reviews Neuroscience, 1 , 125-132. [http://www.nature.com/nrn/journal/v1/n2/full/nrn1100_125a_fs.html link]

* E. Smith and M. S. Lewicki, Efficient Auditory Coding, Nature, 439 (7079), 2006. [http://www.nature.com/nature/journal/v439/n7079/abs/nature04485.html link]

: News and Views: DeWeese and Zador, "Neurobiology: Efficiency measures" [http://www.nature.com/nature/journal/v439/n7079/full/439920a.html link]

* Olshausen BA, Field DJ. Sparse Coding of Sensory Inputs. Curr Op in Neurobiology, 14: 481-487 (2004). [http://redwood.psych.cornell.edu/papers/current-opinion.pdf link]

* Coding and computation with neural spike trains. W Bialek & A Zee, J. Stat. Phys. 59, 103–115 (1990). [http://www.princeton.edu/~wbialek/our_papers/bialek+zee_90.pdf link]

* Selection from: Spikes: Exploring the Neural Code. F Rieke, D Warland, R de Ruyter van Steveninck & W Bialek (MIT Press, Cambridge, 1997). [http://melvyl.cdlib.org/F/?func=find-b&base=CDL90&request=0-262-18174-6&find_code=020 link]

==== Cortical Microcircuit/Universal Cortical Algorithm ====

* <big>✔</big> Vernon Mountcastle (1978), "An Organizing Principle for Cerebral Function: The Unit Model and the Distributed System", The Mindful Brain (Gerald M. Edelman and Vernon B. Mountcastle, eds.) Cambridge, MA: MIT Press [http://redwood.berkeley.edu/~amir/pdf/Mountcastle78.pdf link]

* <big>✔</big> Rodney J. Douglas, Kevan A.C. Martin, Neural Circuits of the Neocortex, Annual Review of Neuroscience 2004 27, 419-451 [http://arjournals.annualreviews.org/doi/full/10.1146/annurev.neuro.27.070203.144152 link]

* <big>✔</big> Selection from: Hawkins, J., On Intelligence (Chapter 6) [http://redwood.berkeley.edu/~amir/pdf/HawkinsChap6.pdf link]

* <big>✔</big> Henry Markram, The Blue Brain Project, Nature Neuroscience, 7:153-160, 2006. [http://www.nature.com/nrn/journal/v7/n2/pdf/nrn1848.pdf link]

* Douglas RJ, Martin KAC Whitteridge D. (1989) A canonical microcircuit for neocortex. Neural Computation 1: 480-488. [http://scholar.google.com/scholar?hl=en&lr=&q=douglas+martin+whitteridge+neural+comput+1989&btnG=Search link] (the [http://www.archive.org/details/redwood_center_inaugural_symposium_08 related talk] at the Redwood symposium)

* Cross-modal plasticity in cortical development: differentiation and specification sensory cortex, by Mriganka Sur, Sarah L. Pallas and Anna W. Roe. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=1694329&dopt=Citation link]

* Poggio, T. and E. Bizzi. Generalization in Vision and Motor Control, Nature, Vol. 431, 768-774, 2004. [http://www.nature.com/nature/journal/v431/n7010/abs/nature03014.html link]

* Marr D, "A Theory for Cerebral Neocortex", Proc Roy Soc London(B), 176, 161-234, 1970. [http://adsabs.harvard.edu/abs/1970RSPSB.176..161M link]

==== Feedback, Hierarchical Organization, Generative Models ====

* <big>✔</big> David Mumford, Neuronal Architectures for Pattern-theoretic Problems, in "Large-Scale Neuronal Theories of the Brain", C.Koch & J.Davis, editors, MIT Press, 1994, pp.125-152. [http://redwood.berkeley.edu/~amir/pdf/Neuronal_Mumford.pdf link]

* <big>✔</big> RPN Rao. Bayesian inference and attention in the visual cortex. Neuroreport 16(16), 1843-1848, 2005. [http://www.cs.washington.edu/homes/rao/nreport_bayes_atten05.pdf link]

* TS Lee, D Mumford Hierarchical Bayesian inference in the visual cortex, Journal of the Optical Society of America A, 2003 [http://scholar.google.com/url?sa=U&q=http://www.dam.brown.edu/people/mumford/Papers/JOSALeeMumford.pdf link]

* Felleman, DJ and Van Essen, DC (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex, 1, 1-47. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=1822724&dopt=Abstract link]

==== Manifold Learning ====

* <big>✔</big> Richard Durbin & Graeme Mitchison, A dimension reduction framework for understanding cortical maps, Nature 343, 644 - 647 (15 February 1990) [http://www.nature.com/nature/journal/v343/n6259/abs/343644a0.html link]

* <big>✔</big> Tal Kenet, Dmitri Bibitchkov, Misha Tsodyks, Amiram Grinvald and Amos Arieli, Spontaneously emerging cortical representations of visual attributes, Nature 425, 954-956 (30 October 2003) [http://www.nature.com/nature/journal/v425/n6961/full/nature02078.html link]

* Selection from: Self-Organizing and Associative Memory, T Kohonen - Japanese translation 2nd Edition, Splinger-Verlag Tokyo, 1994 [http://citeseer.ist.psu.edu/context/2930/0 link]

* JB Tenenbaum, V de Silva, JC Langford, A Global Geometric Framework for Nonlinear Dimensionality Reduction, Science, 2000 [http://www.sciencemag.org/cgi/content/full/290/5500/2319 link]

* ST Roweis, LK Saul, Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science, 2000 [http://www.sciencemag.org/cgi/content/full/290/5500/2323 link]

* Nicholas V. Swindale, Doron Shoham, Amiram Grinvald, Tobias Bonhoeffer & Mark Hübener, Visual cortex maps are optimized for uniform coverage, Nature Neuroscience 3, 822 - 826 (2000) [http://www.nature.com/neuro/journal/v3/n8/full/nn0800_822.html link]

Background reading on SOMs
* Neural Computation and Self-Organizing Maps - An Introduction, by Helge Ritter, Thomas Martinetz, and Klaus Schulten, Addison-Wesley, New York, 1992, [http://www.ks.uiuc.edu/Services/Class/PHYS498TBP/spring2002/neuro_4.pdf Kohonen's Network Model] [http://www.ks.uiuc.edu/Services/Class/PHYS498TBP/spring2002/neuro_book.html Contents]

==== Plasticity, Hebbian Learning ====

* Dan, Y. and Poo, M.-m. (2004). Spike timing-dependent plasticity of neural circuits. Neuron 44, 23-30 [http://www.ling.sinica.edu.tw/paper-Mu-ming%2520Poo-2.pdf link]

* Abbott LF, Nelson SB. (2000) Synaptic plasticity: taming the beast. Nat Neurosci. 3 Suppl:1178-83. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=11127835&dopt=Abstract link]

* P. Foldiak, Forming sparse representations by local anti-Hebbian learning, Biological Cybernetics, vol. 64, pp. 165-170, 1990. [http://www.st-andrews.ac.uk/~pf2/FoldiakSparseBC90.pdf link]

* M. Tsodyks, Spike-timing-dependent synaptic plasticity–The long road towards understanding neuronal mechanisms Trends in Neuroscience, 2002. [http://dx.doi.org/10.1016/S0166-2236(02)02294-4 link]

* Saudargienne A, Porr B, and Worgotter F. How the shape of pre- and postsynaptic signals can influence STDP: a biophysical model. Neural Comp 16: 595–625, 2004. [http://neco.mitpress.org/cgi/content/abstract/16/3/595 link]

* Seung, HS (2000) Half a century of Hebb. Nat. Neurosc. Suppl: 1166. [http://www.nature.com/cgi-taf/DynaPage.taf?file=/neuro/journal/v3/n11s/full/nn1100_1166.html link]

* H. S. Seung. Learning in spiking neural networks by reinforcement of stochastic synaptic transmission. Neuron 40, 1063-1073 (2003). [http://hebb.mit.edu/people/seung/papers/Neuron18Dec03.pdf link]

* Hinton, G. E. and Sejnowski, T. J. (1986), Learning and relearning in Boltzmann machines. In Rumelhart, D. E. and McClelland, J. L., editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations, MIT Press, Cambridge, MA. [http://portal.acm.org/citation.cfm?coll=GUIDE&dl=GUIDE&id=104291 link]

==== Oscillations ====

* Gray, CM (1999) The temporal correlation hypothesis of visual feature integration: still alive and well. Neuron. 1999, 24(1):31-47, 111-25. Review. [http://scholar.google.com/url?sa=U&q=http://lifesci.rutgers.edu/~auerbach/Gray%2520binding.pdf link]

* Neuron Special Issue on Oscillations, 1999, 24(1) [http://www.sciencedirect.com/science?_ob=IssueURL&_tockey=%23TOC%237054%231999%23999759998%23575347%23FLA%23&_auth=y&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=fcf1d593bba077aaec2c4f34adbe071d link]

==== Associative Memory ====

* J Hopfield. Neural Networks and Physical Systems with Emergent Collective Computational Abilities. PNAS, 79:2554-2558 (1982) [http://www.pnas.org/cgi/content/abstract/79/8/2554 link]

* G. Palm. On the storage capacity of an associative memory with randomly distributed storage elements. Biol. Cybernetics. 36:19-31 (1980). [http://www.springerlink.com/link.asp?id=wp7q375127415311 link]

* Selection from: Self-Organizing and Associative Memory, T Kohonen - Japanese translation 2nd Edition, Splinger-Verlag Tokyo, 1994

==== Models of Invariance ====

* Olshausen BA, Anderson CH, Van Essen DC (1993). A Neurobiological Model of Visual Attention and Invariant Pattern Recognition Based on Dynamic Routing of Information, The Journal of Neuroscience, 13(11), 4700-4719. [http://redwood.berkeley.edu/bruno/papers/ link]

* K Fukushima, Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biological Cybernetics (Historical Archive), Volume 36, Issue 4, Apr 1980, Pages 193 - 202 [http://www.springerlink.com/link.asp?id=r6g5w3tt54528137 link]

* P. Foldiak, Learning invariance from transformation sequences, Neural Computation, vol. 3, pp. 194-200, 1991. [http://www.st-andrews.ac.uk/~pf2/FoldiakInvarianceLearningNC91.pdf link]

* L Wiskott. Slow Feature Analysis: Unsupervised Learning of Invariances. Neural Computation. 2002;14:715-770. [http://neco.mitpress.org/cgi/content/abstract/14/4/715 link]

==== Active Perception-sensorimotor loops ====

* Churchland P, Ramachandran VS, Sejnowski TJ. Large-Scale Neuronal Theories of the Brain: Chapter 2, Critique of Pure Vision, 1994. [http://cnl.salk.edu/~terry/PDF/PureVision.94.pdf link]

* Oregan JK, Noe A. A sensorimotor account of vision and visual consciousness, BBS (2001) 24:939-1031. [http://ist-socrates.berkeley.edu/~noe/oregan.noe.pdf link]

* Philipona D, O'Regan JK, Nadal JP. Is There Something Out There? Inferring Space from Sensorimotor Dependencies. Neural Computation 2003;15:2029-2049. [http://neco.mitpress.org/cgi/content/abstract/15/9/2029 link]

==== Theories of the Ventral Stream ====

* Ullman "Streams and Counter Streams", chapter in Large Scale Neuronal Theories of the Brain
* Riesenhuber, M. and T. Poggio. How Visual Cortex Recognizes Objects: The Tale of the Standard Model. In: The Visual Neurosciences, (Eds. L.M. Chalupa and J.S. Werner), MIT Press, Cambridge, MA, Vol. 2, 1640-1653, 2003. [http://cbcl.mit.edu/projects/cbcl/publications/ps/max-vis-cortex-03.pdf link]

* Rolls,E.T. (1997) A neurophysiological and computational approach to the functions of the temporal lobe cortical visual areas in invariant object recognition. Chapter 9, pp. 184-220 in Computational and Psychophysical Mechanisms of Visual Coding, eds. M.Jenkin and L.Harris. Cambridge University Press: Cambridge. [http://www.cns.ox.ac.uk/publications.html link]

==== Theories of Hippocampus ====

* Becker, S. (2005) "A computational principle for hippocampal learning and neurogenesis". Hippocampus 15(6):722-738. [http://www.science.mcmaster.ca/Psychology/sb.html link]

* Leutgeb, S., Leutgeb, J.K., Moser, M.-B., and Moser, E.I. (2005). Place cells, spatial maps and the population code for memory. Current Opinion in Neurobiology, 15, 738-746. [http://www.cbm.ntnu.no/publications link]

* [http://www.bris.ac.uk/synaptic/research/projects/memory/spatialmem.htm place cells]

==== Motor System ====

* Körding, KP. and Wolpert, D. (2004) Bayesian Integration in Sensorimotor Learning, Nature 427:244-247. [http://www.koerding.com/pubs/koerdingNature2004.pdf link]

Suggestion Board

2006-03-07T02:55:01Z

TCN: /* Post Your Suggested Topics or Paper */

== Post Your Suggested Topics or Paper (click on Edit)==

Here's an example for formating:
===Topic===
* E. Smith and M. S. Lewicki, Efficient Auditory Coding, Nature, 439 (7079), 2006. [http://www.nature.com/nature/journal/v439/n7079/abs/nature04485.html link]

: News and Views: DeWeese and Zador, "Neurobiology: Efficiency measures". [http://www.nature.com/nature/journal/v439/n7079/full/439920a.html link]