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== Topics in Computational Neuroscience ==
== Topics in Computational Neuroscience ==
For ideas about some interesting papers to discuss have look here [[TCN Paper Ideas]]


=== Overview ===
=== 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.
This journal club 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. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.


'''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.''
It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.


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.
If you have questions, please email the club organizer (2017) [mailto:edodds@berkeley.edu?Subject=Redwood%TCN%Journal%20Club  Eric Dodds]


=== E-mail List ===
=== Time and Location ===
 
(Fall 2017)
redwood_tcn at lists.berkeley.edu
'''4pm-5pm''' almost every Wednesday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on changes to meeting dates.
 
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 ===
=== Guidelines for Presenting Papers ===
Each person that selects a paper should present, in about 10 minutes (no slides):
Each person that selects a paper should present, in about 15-30 minutes:
* an executive summary
* an executive summary
* an outline of the key points, ideas, or contributions
* an outline of the key points, ideas, or contributions
* relevant background information
* a description of the key figures
* a description of the key figures
* what you took away from the paper
* what you took away from the paper
* some potential questions for discussion
* some potential questions for discussion
* you are encouraged to use whatever method to present (slides, puppets, etc.)


=== Make a Topic or Paper Suggestion ===
=== E-mail List ===
 
[[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>&#10004;</big> indicates papers we've read.
 
==== Early Work ====
 
* <big>&#10004;</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>&#10004;</big> Lettvin, Maturana, McCulloch, Pitts, "What the Frog's Eye Tells the Frog's Brian" [http://jerome.lettvin.info/WhatTheFrogsEyeTellsTheFrogsBrain.pdf link]
 
* <big>&#10004;</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]
To subscribe to the journal club email list, send an email to redwood_tcn+subscribe@lists.berkeley.edu. You will receive emails twice a week about papers that will be covered in the next meeting.


==== Coding ====
===Fall 2017===
* [December 13] <font color="##ff0000">Ryan Zarcone</font>
* [December 6] <font color="##ff0000">Danny Weitekamp</font>
* [November 29] <font color="##ff0000">Eric Weiss</font>
* [November 15] <font color="##ff0000">Bruno Olshausen</font> - Sara Sabour, Nicholas Frosst, and Geoffrey E. Hinton. "Dynamic Routing Between Capsules." [https://arxiv.org/abs/1710.09829]
* [November 8] <font color="##ff0000">Charles Frye</font> - Jeffrey Pennington and Yasaman Bahri. "Geometry of Neural Network Loss Surfaces via Random Matrix Theory." [http://proceedings.mlr.press/v70/pennington17a/pennington17a.pdf]
* [November 1] <font color="##ff0000">Shariq Mobin</font> - Matthew James Johnson <i>et al.</i> "Composing graphical models with neural networks for structured representations and fast inference." [http://papers.nips.cc/paper/6378-composing-graphical-models-with-neural-networks-for-structured-representations-and-fast-inference]
* [October 25] <font color="##ff0000">Vasha DuTell</font> - Marius Pachitariu and Maneesh Sahani. "Visual motion computation in recurrent neural networks." [https://www.biorxiv.org/content/early/2017/01/09/099101]
* [October 18] <font color="##ff0000">Pratik Sachdeva</font> - Ashok Litwin-Kumar & Brent Doiron. "Slow dynamics and high variability in balanced cortical networks with clustered connections." [http://www.nature.com/neuro/journal/v15/n11/full/nn.3220.html?foxtrotcallback=true]
* [October 4] <font color="##ff0000">Mayur Mudigonda</font> - Ingmar Kanitscheider & Ila Fiete. "Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems." [https://arxiv.org/abs/1609.09059]
* [September 27] <font color="##ff0000">Spencer Kent</font> - Jiajun Wu, Joshua B. Tenenbaum, and Pushmeet Kohli. "Neural Scene De-rendering" [http://openaccess.thecvf.com/content_cvpr_2017/papers/Wu_Neural_Scene_De-Rendering_CVPR_2017_paper.pdf]
* [September 13] <font color="##ff0000">Guy Isely</font> - Saurabh Gupta, James Davidson, Sergey Levine, Rahul Sukthankar, and Jitendra Malik. "Cognitive Mapping and Planning for Visual Navigation." [https://arxiv.org/abs/1702.03920]


* <big>&#10004;</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]
===Summer 2017===
* [August 22] <font color="##ff0000">Dylan Paiton</font> - Honghao Shan,  Lingyun Zhang, and Garrison Cottrell. "Recursive ICA" [http://papers.nips.cc/paper/3018-recursive-ica.pdf]
* [August 8] <font color="##ff0000">Guy Isely</font> - Max Jaderberg, Wojciech Marian Czarnecki, Simon Osindero, Oriol Vinyals, Alex Graves, David Silver, and Koray Kavukcuoglu. "Decoupled Neural Interfaces using Synthetic Gradients." [https://arxiv.org/abs/1608.05343]
* [August 1] <font color="##ff0000">Alex Anderson</font> - Rahul G. Krishnan, Uri Shalit, and David Sontag. "Structured Inference Networks for Nonlinear State Space Models." [https://arxiv.org/abs/1609.09869]
* [July 25] Canceled
* [July 18] Canceled for CRCNS course
* [July 11] Canceled for CRCNS course
* [July 4] Holiday
* [June 27] <font color="##ff0000">Everyone</font> - Ravid Shwartz-Ziv and Naftali Tishby. "Opening the Black Box of Deep Neural Networks via Information." [https://arxiv.org/abs/1703.00810]
* [June 20] <font color="##ff0000">Sean Mackesey</font> - R. Ellen Ambrose, Brad E. Pfeiffer, and David J. Foster. "Reverse replay of hippocampal place cells is uniquely modulated by changing reward." Neuron 91.5 (2016): 1124-1136.
* [June 13] <font color="##ff0000">Neha Wadia</font> - Dmitriy Aronov, Rhino Nevers, and David W. Tank. "Mapping of a non-spatial dimension by the hippocampal–entorhinal circuit." [https://www.nature.com/nature/journal/v543/n7647/pdf/nature21692.pdf]
* [June 6] <font color="##ff0000">Charles Garfinkle</font> - Plant neurobiology [https://www.researchgate.net/profile/Stefano_Mancuso/publication/6942772_Plant_neurobiology_an_integrated_view_of_plant_signaling/links/0fcfd5087fafc3b3df000000.pdf][https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5047902/pdf/fpsyg-07-01505.pdf] [http://biorxiv.org/content/biorxiv/early/2017/03/30/122358.full.pdf]
* [May 30] <font color="##ff0000">Jesse Livezey</font> - Readings on (ab)uses of machine learning in policing [https://medium.com/@blaisea/physiognomys-new-clothes-f2d4b59fdd6a] [https://whitecollar.thenewinquiry.com/static/whitepaper.pdf] [http://www.americanrhetoric.com/speeches/robertoppenheimeratomicbomb.htm] etc


* <big>&#10004;</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]
===Spring 2017===
* [May 16] <font color="##ff0000">Various?</font> - Redwood NIPS submissions 2017
* [May 9] <font color="##ff0000">James Arnemann</font> - van den Oord et al. (2016) Wavenet: A Generative Model for Raw Audio. [https://deepmind.com/blog/wavenet-generative-model-raw-audio/]
* [May 2] <font color="##ff0000">Pratik Sachdeva</font> - Ashok Litwin-Kumar, Kameron Decker Harris, Richard Axel, Haim Sompolinsky, and L.F. Abbott. (2017) Optimal Degrees of Synaptic Connectivity.[http://www.sciencedirect.com/science/article/pii/S0896627317300545]
* [Apr 25] <font color="##ff0000">Kata Slama</font> - József Fiser, Pietro Berkes, Gergő Orbán, Máté Lengyel. (2010) Statistically optimal perception and learning: from behavior to neural representations [http://www.sciencedirect.com/science/article/pii/S1364661310000045]
* [Apr 18] <font color="##ff0000">Dylan Paiton</font> - Matthew Zeiler & Rob Fergus. (2012) Differentiable Pooling for Hierarchical Feature Learning. [https://arxiv.org/pdf/1207.0151.pdf ]
* [Apr 11] <font color="##ff0000">Eric Weiss</font> - Discussion on computational complexity and regularization in machine learning
* [Apr 4] <font color="##ff0000">Brian Cheung</font> - Jun-Yan Zhu, Taesung Park, Philip Isola, and Alexei A. Efros. (2017) "Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks." [https://arxiv.org/abs/1703.10593]
* [Mar 14] <font color="##ff0000">Ryan Zarcone</font> - Johannes Ballé, Valero Laparra, and Eero P. Simoncelli. (2017) "End-to-end Optimized Image Compression." ICLR 2017. [https://arxiv.org/pdf/1611.01704.pdf]
* [Mar 7] <font color="##ff0000">Eric Dodds</font> - Wiktor Młynarski and Josh McDermott. (2017) "Learning Mid-Level Auditory Codes from Natural Sound Statistics." [https://arxiv.org/pdf/1701.07138v3.pdf]
* [Jan 19] <font color="##ff0000">Kata Slama</font> - Jiefeng Jiang (江界峰), Christopher Summerfield and Tobias Egner. (2016) "Visual Prediction Error Spreads Across Object Features in Human Visual Cortex." [https://doi.org/10.1523/JNEUROSCI.1546-16.2016]


* 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]
===Fall 2016===


: News and Views: DeWeese and Zador, "Neurobiology: Efficiency measures" [http://www.nature.com/nature/journal/v439/n7079/full/439920a.html link]
* [Dec 12] <font color="##ff0000">James Golden</font> - JR Golden et al (2016). Conjectures Regarding the Nonlinear Geometry of Visual Neurons. [http://www.sciencedirect.com/science/article/pii/S0042698915003600]
* [Dec 05] <font color="##ff0000">Sean Mackesey</font> - Kraus et al (2013). Hippocampal "Time Cells": Time Versus Path Integration [http://www.cell.com/neuron/abstract/S0896-6273(13)00317-6]
* [Nov 28] <font color="##ff0000">Chris Warner</font> - Lazer et al. (2010). The coevolution of networks and political attitudes. [https://polisci.osu.edu/sites/polisci.osu.edu/files/_the%20coevolution%20of%20networks%20and%20political%20attitudes_.pdf]
* [Nov 21] <font color="##ff0000">Kohta Ishikawa</font> - Do & Vetterli (2003). Framing pyramids. [https://infoscience.epfl.ch/record/33823/files/DoV03j-2.pdf]
* [Nov 14] <font color="##ff0000">Charles Frye</font> - Wainwright & Jordan. (2008). Graphical models, exponential families, and variational inference. [http://bioinfo.au.tsinghua.edu.cn/member/mzhang/pgm/course.files/3_Supplementary_Reference.pdf]
* [Nov 07] <font color="##ff0000">Petr Jezek</font> - Eslami et al (2016). Attend, Infer, Repeat: Fast Scene Understanding with Generative Models. [https://arxiv.org/pdf/1603.08575v3.pdf]
* [Nov 04] <font color="##ff0000">Yubei Chen</font> - Wainwright & Jordan. (2008). Graphical models, exponential families, and variational inference. [http://bioinfo.au.tsinghua.edu.cn/member/mzhang/pgm/course.files/3_Supplementary_Reference.pdf]
* [Oct 24] <font color="##ff0000">Ryan Zarcone</font> - Weiss et al (2007). Learning Compressed Sensing. [http://www.wisdom.weizmann.ac.il/~/vision/courses/2010_2/papers/allerton-final.pdf]
* [Oct 17] <font color="##ff0000">Neha Wadia</font> - Rajan et al (2016). Recurrent network models of sequence generation and memory. [http://www.sciencedirect.com/science/article/pii/S0896627316001021]
* [Oct 10] <font color="##ff0000">Eric Weiss</font> - Tutorial: Hyperdimensional Representations for Scene Analysis.
* [Oct 03] <font color="##ff0000">Vasha Dutell</font> - Dong & Atick (1995). Temporal decorrelation: a theory of lagged and nonlagged responses in the lateral geniculate nucleus. [http://www.tandfonline.com/doi/abs/10.1088/0954-898X_6_2_003]
* [Sep 26] <font color="##ff0000">Jesse Livezey</font> - Cheung et al (2016). The auditory representation of speech sounds in human motor cortex. [https://elifesciences.org/content/5/e12577]
* [Sep 19] <font color="##ff0000">Brian Cheung</font> - Discussion: The Biological Plausibility of Backpropagation.
* [Sep 12] <font color="##ff0000">Alex Anderson</font> - Jaderberg et al (2016). Decoupled neural interfaces using synthetic gradients. [https://arxiv.org/abs/1608.05343]
* [Sep 05] <font color="##ff0000">Charles Garfinkle</font> - Palmer et al (2015). Predictive information in a sensory population. [http://www.pnas.org/content/112/22/6908.full]


* Olshausen BA, Field DJ. Sparse Coding of Sensory InputsCurr Op in Neurobiology, 14: 481-487 (2004). [http://redwood.psych.cornell.edu/papers/current-opinion.pdf link]
===Summer 2016===
* [August 31] <font color="##ff0000">Spencer Kent</font> - Fu et al (2016). Occlusion boundary detection via deep exploration of context. [https://ps.is.tuebingen.mpg.de/publications/fu-cvpr-2016]
* [August 17] <font color="#ff0000">Paxon Frady</font> - Glasser, M. F., et al. (2016) A Multi-modal parcellation of human cerebral cortex. [http://www.nature.com/nature/journal/v536/n7615/pdf/nature18933.pdf] and Huth, Alexander G., et al. (2016) Natural speech reveals the semantic maps that tile human cerebral cortex. [http://www.nature.com/nature/journal/v532/n7600/pdf/nature17637.pdf]
* [August 10] <font color="#ff0000">Mayur Mudigonda</font> - Salimans, Tim, Diederik P. Kingma, and Max Welling (2015). Markov chain Monte Carlo and variational inference: Bridging the gap. [http://www.jmlr.org/proceedings/papers/v37/salimans15.pdf]
* [August 3] <font color="#ff0000">Pratik Sachdeva</font> - Moreno-Bote, Rubén, et al. (2014). Information-limiting correlations. [http://www.nature.com/neuro/journal/v17/n10/pdf/nn.3807.pdf]
* [July 26] <font color="#ff0000">Shariq Mobin</font> - Karklin, Yan, and Eero P. Simoncelli (2011). Efficient coding of natural images with a population of noisy linear-nonlinear neurons.[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4532291/pdf/nihms711099.pdf]
* [July 20] <font color="#ff0000">Sean Mackesey</font> - Buzsáki, György (2010). Neural syntax: cell assemblies, synapsembles, and readers. [http://www.cell.com/neuron/pdf/S0896-6273(10)00765-8.pdf]
* [July 13] <font color="#ff0000">Katarina Slama and Vasha DuTell</font> - Akam, Thomas, and Dimitri M. Kullmann (2010). Oscillations and filtering networks support flexible routing of information. [http://ac.els-cdn.com/S0896627310004770/1-s2.0-S0896627310004770-main.pdf?_tid=67abdb2c-448b-11e6-8094-00000aacb35d&acdnat=1467927801_0eb47d2d6165fef5d1a1ae39b1b927b2]
* [July 6] <font color="#ff0000">Karl Zipser</font> - Bojarski, Mariusz, et al (2016). End to End Learning for Self-Driving Cars. [https://arxiv.org/pdf/1604.07316v1.pdf]
* [June 29] <font color="#ff0000">Ian Robertson</font> - Hermans, M., & Van Vaerenbergh, T. (2015). Towards Trainable Media: Using Waves for Neural Network-Style Training[http://arxiv.org/pdf/1510.03776v1.pdf]
* [June 22] <font color="#ff0000">Group Discussion</font> - Jonas, Eric, and Konrad Kording. (2016) Could a neuroscientist understand a microprocessor? [http://biorxiv.org/content/biorxiv/early/2016/05/26/055624.full.pdf]
* [June 15] <font color="#ff0000">Spencer Kent, Mayur Mudigonda, and Eric Weiss</font> - Kulkarni, Tejas D., et al (2015). Picture: A probabilistic programming language for scene perception. [https://mrkulk.github.io/www_cvpr15/1999.pdf]
* [June 8] <font color="#ff0000">Jesse Livezey</font> - Bornschein, Jörg, Marc Henniges, and Jörg Lücke (2013). Are V1 simple cells optimized for visual occlusions? A comparative study. [http://journals.plos.org/ploscompbiol/article/asset?id=10.1371%2Fjournal.pcbi.1003062.PDF]
* [June 1] <font color="#ff0000">Yubei Chen</font> - Y. Karklin & M. S. Lewicki (2003). Learning higher-order structures in natural images. [http://yan.karklin.com/pubs/Karklin-Lewicki-NCNS03.pdf]
* [May 25] <font color="#ff0000">Eric Dodds</font> - Y. Karklin, C. Ekanadham, & E.P. Simoncelli (2012). Hierarchical spike coding of sound. [http://papers.nips.cc/paper/4780-hierarchical-spike-coding-of-sound.pdf]


* 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]
===Spring 2016===


* 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]
* [May 11] <font color="#ff0000">NIPS exchange</font>


==== Cortical Microcircuit/Universal Cortical Algorithm ====
* [May 04] <font color="#ff0000">Chris Warner</font> - MEJ Newman (2006). Finding Community Structure in Networks using the Eigenvectors of Matrices [http://arxiv.org/pdf/physics/0605087.pdf]


* <big>&#10004;</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]
* [Apr 27] <font color="#ff0000">Mr. (Alex) Anderson</font> - E Ahissar, A Arieli (2012). Seeing Via Miniature Eye Movements- A Dynamic Hypothesis for Vision [http://www.frontiersin.org/Journal/DownloadFile/1/91957/32594/1/21/fncom-06-00089_pdf]


* <big>&#10004;</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]
* [Apr 20] <font color="#ff0000">Charles Frye, Ryan Zarcone, Brian Cheung</font> - RM Neal, GE Hinton (1998). A View of the EM Algorithm That Justifies Incremental, Sparse, and Other Variants [http://ftp.cs.utoronto.ca/cs/ftp/pub/radford/emk.pdf]


* <big>&#10004;</big> Selection from: Hawkins, J., On Intelligence (Chapter 6) [http://redwood.berkeley.edu/~amir/pdf/HawkinsChap6.pdf link]
* [Apr 13] <font color="#ff0000">Kohta Ishikawa</font> - C Zetzsche, U Nuding (2009). Nonlinear and Higher-Order Approaches to the Encoding of Natural Scenes [http://www.tandfonline.com/doi/pdf/10.1080/09548980500463982]


* <big>&#10004;</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]
* [Apr 06] <font color="#ff0000">Spencer Kent</font> - IJ Goodfellow, J Pouget-Abadie, M Mirza, B Xu, D Warde-Farley, Y Bengio (2014). Generative Adversarial Nets [http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf]


* 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)
* [Mar 30] <font color="#ff0000">Guy Isley</font> - R Chaudhuri, A Bernacchia, XJ Wang (2014). A Diversity of Localized Timescales in Network Activity [http://elifesciences.org/content/3/e01239v1]


* 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]
* [Mar 23] <font color="#ff0000">Brian Cheung</font> - DJ Rezende, S Mohamed, I Danihelka, K Gregor, D Wierstra (2016). One-Shot Generalization in Deep Generative Models [http://arxiv.org/abs/1603.05106]


* 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]
* [Mar 16] <font color="#ff0000">Yubei Chen</font> - A Beck, M Teboulle (2009). A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems [http://www-ai.cs.uni-dortmund.de/LEHRE/VORLESUNGEN/LOPT/WS14/papers/FISTA.pdf]


* 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]
* [Mar 09] <font color="#ff0000">Sean Mackesey</font> - P Fries (2015). Rhythms for Cognition: Communication Through Coherence [http://www.sciencedirect.com/science/article/pii/S0896627315008235]


==== Feedback, Hierarchical Organization, Generative Models ====
* [Mar 02] <font color="#ff0000">Charles Garfinkle</font> - BB Ujfalussy, JK Makara, T Branco, M Lengyel (2015). Dendritic Nonlinearities are Tuned for Efficient Spike-Based Computations in Cortical Circuits [http://elifesciences.org/content/4/e10056v1#api_box]


* <big>&#10004;</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]
* [Feb 24] <font color="#ff0000">Eric Weiss</font> - TS Lee, D Mumford (2003). Hierarchical Bayesian Inference in the Visual Cortex [https://dash.harvard.edu/bitstream/handle/1/3637109/Mumford_HierarchBayesInfer.pdf]


* <big>&#10004;</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]
* [Feb 17] <font color="#ff0000">Paxon Frady</font> - C Eliasmith et al. (2012). A Large-Scale Model of the Functioning Brain [https://cs.uwaterloo.ca/~cdimarco/pdf/cogsci600/Eliasmith2013a.pdf]


* 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]
* [Feb 10] Cancelled - EECS Colloquium


* 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]
* [Feb 03] <font color="#ff0000">Charles Frye</font> - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [http://arxiv.org/abs/1407.0973]


==== Manifold Learning ====
* [Jan 21] <font color="#ff0000">Jesse Livezy and Andrew Berger</font> - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [http://www.worldscientific.com/doi/abs/10.1142/S0129065714400012]


* <big>&#10004;</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]
* [Jan 14] <font color="#ff0000">Daniel Toker</font> - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [http://rsta.royalsocietypublishing.org/content/369/1952/3748.short]


* <big>&#10004;</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]
===Fall 2015===


* 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]
* [Dec 17] <font color="#ff0000">Charles Frye</font> - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [http://www.sciencemag.org/content/350/6266/1332.abstract]


* 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]
* [Dec 03] <font color="#ff0000">Omer Hazon</font> - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [http://papers.nips.cc/paper/5269-expectation-backpropagation-parameter-free-training-of-multilayer-neural-networks-with-continuous-or-discrete-weights.pdf]


* ST Roweis, LK Saul, Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science, 2000 [http://www.sciencemag.org/cgi/content/full/290/5500/2323 link]
* [Nov 26] Thanksgiving break


* 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]
* [Nov 19] <font color="#ff0000">Eric Dodds</font> - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [http://www.nature.com/nature/journal/v439/n7079/full/nature04485.html]


Background reading on SOMs
* [Nov 12] <font color="#ff0000">Vasha Dutell</font> - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [http://www.jneurosci.org/content/35/29/10412.full]
* 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 ====
=== Summer 2014 ===


* 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]
* [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [http://buzsakilab.com/content/PDFs/Mizuseki2014.pdf]


* 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]
* [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [http://arxiv.org/pdf/cond-mat/9512035v1.pdf]


* 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]
* [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [http://cocosci.berkeley.edu/tom/papers/neuralIS.pdf]


* 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]
* [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [http://www.sciencedirect.com/science/article/pii/S0896627313002675]


* 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]
* [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [http://www.neurobio.ucla.edu/~dbuono/PDFs/LajeBuonomano_NatNeurosci_13.pdf]


* 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]
=== Spring 2014 ===


* 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]
* [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]


* 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]
=== Fall 2013 ===


==== Oscillations ====
* [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657838/#!po=22.7273]


* 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]
=== Summer 2013 ===


* 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]
* [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [http://www.math.unl.edu/~ccurto2/papers/cell-groups.pdf]


==== Associative Memory ====
=== Spring 2013 ===


* 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]
* [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=Y&aulast=Burak&atitle=Accurate+path+integration+in+continuous+attractor+network+models+of+grid+cells&id=doi:10.1371/journal.pcbi.1000291&title=PLoS+Computational+Biology&volume=5&issue=2&date=2009&spage=e1000291&issn=1553-734X] [http://www.jneurosci.org/content/16/6/2112.full.pdf]


* 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]
* [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [http://www.clm.utexas.edu/fietelab/Papers/nn.2901.pdf]  


* Selection from: Self-Organizing and Associative Memory, T Kohonen - Japanese translation 2nd Edition, Splinger-Verlag Tokyo, 1994
* [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=NJ&aulast=Killian&atitle=A+map+of+visual+space+in+the+primate+entorhinal+cortex&id=doi:10.1038/nature11587&title=Nature&volume=491&issue=7426&date=2012&spage=761&issn=0028-0836]


==== Models of Invariance ====
* [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [http://www.pnas.org/content/108/suppl.3/15624.full.pdf+html]


* 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]
* [Mar 6] Grady 2006 - Random Walks for Image Segmentation [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1704833&tag=1]


* 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]
* [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [http://scholar.google.com/scholar?hl=en&q=Parallels+between+sensory+and+motor+information+processing&btnG=&as_sdt=1%2C5&as_sdtp=]


* 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]
* [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [http://arxiv.org/pdf/1205.1828v1.pdf]


* 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]
* [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [http://ucelinks.cdlib.org:8888/sfx_local?sid=google&auinit=F&aulast=Girosi&atitle=An+equivalence+between+sparse+approximation+and+support+vector+machines&id=doi:10.1162/089976698300017269&title=Neural+computation&volume=10&issue=6&date=1998&spage=1455&issn=0899-7667][http://18.7.29.232/bitstream/handle/1721.1/7289/AIM-1606.pdf?sequence=2]


==== Active Perception-sensorimotor loops ====
* [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [http://www.jneurosci.org/content/16/22/7376.full.pdf+html]
Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [http://www.jneurosci.org/content/32/40/13971.full.pdf+html]


* 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]
* [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [http://books.nips.cc/papers/files/nips25/NIPS2012_1264.pdf] [http://web.eecs.umich.edu/~kulesza/pubs/thesis.pdf]


* 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]
* [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [http://www.pnas.org/content/109/6/2138.long]


* 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]
=== Fall 2012 ===


==== Theories of the Ventral Stream ====
* [Aug 22] Maass et al. - Liquid State Computing [http://www.igi.tugraz.at/maass/psfiles/130.pdf] [http://www.igi.tugraz.at/maass/psfiles/186.pdf]


* Ullman "Streams and Counter Streams", chapter in Large Scale Neuronal Theories of the Brain
* [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [http://www.mitpressjournals.org/doi/pdfplus/10.1162/NECO_a_00311]
* 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]
* [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [http://www.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf]


==== Theories of Hippocampus ====
* [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [http://www.phys.psu.edu/~collins/RNI/Quian_Quiroga_concept_cell_review_2012.pdf]


* 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]
* [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [http://www.sns.ias.edu/pitp/2012files/Are_Biological_Systems.pdf]


* 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]
* [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [http://www-personal.umich.edu/~mejn/courses/2006/cmplxsys899/powerlaws.pdf]


* [http://www.bris.ac.uk/synaptic/research/projects/memory/spatialmem.htm place cells]
* [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [http://www.nature.com/neuro/journal/v7/n9/pdf/nn1309.pdf]


==== Motor System ====
=== [[Past TCN Papers]] ===


* 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]]===

Latest revision as of 07:40, 13 November 2017

Topics in Computational Neuroscience

For ideas about some interesting papers to discuss have look here TCN Paper Ideas

Overview

This journal club 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. It provides a broad survey of literature from theoretical and computational neuroscience. Readings will combine both seminal works and recent theories. We meet for one session each week.

It is possible to take this seminar for credit. If you would like to do so, please mention during journal club.

If you have questions, please email the club organizer (2017) Eric Dodds

Time and Location

(Fall 2017) 4pm-5pm almost every Wednesday in the Redwood Center conference room (560 Evans). Please sign up to the email list (below) for announcements on changes to meeting dates.

Guidelines for Presenting Papers

Each person that selects a paper should present, in about 15-30 minutes:

  • an executive summary
  • an outline of the key points, ideas, or contributions
  • relevant background information
  • a description of the key figures
  • what you took away from the paper
  • some potential questions for discussion
  • you are encouraged to use whatever method to present (slides, puppets, etc.)

E-mail List

To subscribe to the journal club email list, send an email to redwood_tcn+subscribe@lists.berkeley.edu. You will receive emails twice a week about papers that will be covered in the next meeting.

Fall 2017

  • [December 13] Ryan Zarcone
  • [December 6] Danny Weitekamp
  • [November 29] Eric Weiss
  • [November 15] Bruno Olshausen - Sara Sabour, Nicholas Frosst, and Geoffrey E. Hinton. "Dynamic Routing Between Capsules." [1]
  • [November 8] Charles Frye - Jeffrey Pennington and Yasaman Bahri. "Geometry of Neural Network Loss Surfaces via Random Matrix Theory." [2]
  • [November 1] Shariq Mobin - Matthew James Johnson et al. "Composing graphical models with neural networks for structured representations and fast inference." [3]
  • [October 25] Vasha DuTell - Marius Pachitariu and Maneesh Sahani. "Visual motion computation in recurrent neural networks." [4]
  • [October 18] Pratik Sachdeva - Ashok Litwin-Kumar & Brent Doiron. "Slow dynamics and high variability in balanced cortical networks with clustered connections." [5]
  • [October 4] Mayur Mudigonda - Ingmar Kanitscheider & Ila Fiete. "Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems." [6]
  • [September 27] Spencer Kent - Jiajun Wu, Joshua B. Tenenbaum, and Pushmeet Kohli. "Neural Scene De-rendering" [7]
  • [September 13] Guy Isely - Saurabh Gupta, James Davidson, Sergey Levine, Rahul Sukthankar, and Jitendra Malik. "Cognitive Mapping and Planning for Visual Navigation." [8]

Summer 2017

  • [August 22] Dylan Paiton - Honghao Shan, Lingyun Zhang, and Garrison Cottrell. "Recursive ICA" [9]
  • [August 8] Guy Isely - Max Jaderberg, Wojciech Marian Czarnecki, Simon Osindero, Oriol Vinyals, Alex Graves, David Silver, and Koray Kavukcuoglu. "Decoupled Neural Interfaces using Synthetic Gradients." [10]
  • [August 1] Alex Anderson - Rahul G. Krishnan, Uri Shalit, and David Sontag. "Structured Inference Networks for Nonlinear State Space Models." [11]
  • [July 25] Canceled
  • [July 18] Canceled for CRCNS course
  • [July 11] Canceled for CRCNS course
  • [July 4] Holiday
  • [June 27] Everyone - Ravid Shwartz-Ziv and Naftali Tishby. "Opening the Black Box of Deep Neural Networks via Information." [12]
  • [June 20] Sean Mackesey - R. Ellen Ambrose, Brad E. Pfeiffer, and David J. Foster. "Reverse replay of hippocampal place cells is uniquely modulated by changing reward." Neuron 91.5 (2016): 1124-1136.
  • [June 13] Neha Wadia - Dmitriy Aronov, Rhino Nevers, and David W. Tank. "Mapping of a non-spatial dimension by the hippocampal–entorhinal circuit." [13]
  • [June 6] Charles Garfinkle - Plant neurobiology [14][15] [16]
  • [May 30] Jesse Livezey - Readings on (ab)uses of machine learning in policing [17] [18] [19] etc

Spring 2017

  • [May 16] Various? - Redwood NIPS submissions 2017
  • [May 9] James Arnemann - van den Oord et al. (2016) Wavenet: A Generative Model for Raw Audio. [20]
  • [May 2] Pratik Sachdeva - Ashok Litwin-Kumar, Kameron Decker Harris, Richard Axel, Haim Sompolinsky, and L.F. Abbott. (2017) Optimal Degrees of Synaptic Connectivity.[21]
  • [Apr 25] Kata Slama - József Fiser, Pietro Berkes, Gergő Orbán, Máté Lengyel. (2010) Statistically optimal perception and learning: from behavior to neural representations [22]
  • [Apr 18] Dylan Paiton - Matthew Zeiler & Rob Fergus. (2012) Differentiable Pooling for Hierarchical Feature Learning. [23]
  • [Apr 11] Eric Weiss - Discussion on computational complexity and regularization in machine learning
  • [Apr 4] Brian Cheung - Jun-Yan Zhu, Taesung Park, Philip Isola, and Alexei A. Efros. (2017) "Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks." [24]
  • [Mar 14] Ryan Zarcone - Johannes Ballé, Valero Laparra, and Eero P. Simoncelli. (2017) "End-to-end Optimized Image Compression." ICLR 2017. [25]
  • [Mar 7] Eric Dodds - Wiktor Młynarski and Josh McDermott. (2017) "Learning Mid-Level Auditory Codes from Natural Sound Statistics." [26]
  • [Jan 19] Kata Slama - Jiefeng Jiang (江界峰), Christopher Summerfield and Tobias Egner. (2016) "Visual Prediction Error Spreads Across Object Features in Human Visual Cortex." [27]

Fall 2016

  • [Dec 12] James Golden - JR Golden et al (2016). Conjectures Regarding the Nonlinear Geometry of Visual Neurons. [28]
  • [Dec 05] Sean Mackesey - Kraus et al (2013). Hippocampal "Time Cells": Time Versus Path Integration [29]
  • [Nov 28] Chris Warner - Lazer et al. (2010). The coevolution of networks and political attitudes. [30]
  • [Nov 21] Kohta Ishikawa - Do & Vetterli (2003). Framing pyramids. [31]
  • [Nov 14] Charles Frye - Wainwright & Jordan. (2008). Graphical models, exponential families, and variational inference. [32]
  • [Nov 07] Petr Jezek - Eslami et al (2016). Attend, Infer, Repeat: Fast Scene Understanding with Generative Models. [33]
  • [Nov 04] Yubei Chen - Wainwright & Jordan. (2008). Graphical models, exponential families, and variational inference. [34]
  • [Oct 24] Ryan Zarcone - Weiss et al (2007). Learning Compressed Sensing. [35]
  • [Oct 17] Neha Wadia - Rajan et al (2016). Recurrent network models of sequence generation and memory. [36]
  • [Oct 10] Eric Weiss - Tutorial: Hyperdimensional Representations for Scene Analysis.
  • [Oct 03] Vasha Dutell - Dong & Atick (1995). Temporal decorrelation: a theory of lagged and nonlagged responses in the lateral geniculate nucleus. [37]
  • [Sep 26] Jesse Livezey - Cheung et al (2016). The auditory representation of speech sounds in human motor cortex. [38]
  • [Sep 19] Brian Cheung - Discussion: The Biological Plausibility of Backpropagation.
  • [Sep 12] Alex Anderson - Jaderberg et al (2016). Decoupled neural interfaces using synthetic gradients. [39]
  • [Sep 05] Charles Garfinkle - Palmer et al (2015). Predictive information in a sensory population. [40]

Summer 2016

  • [August 31] Spencer Kent - Fu et al (2016). Occlusion boundary detection via deep exploration of context. [41]
  • [August 17] Paxon Frady - Glasser, M. F., et al. (2016) A Multi-modal parcellation of human cerebral cortex. [42] and Huth, Alexander G., et al. (2016) Natural speech reveals the semantic maps that tile human cerebral cortex. [43]
  • [August 10] Mayur Mudigonda - Salimans, Tim, Diederik P. Kingma, and Max Welling (2015). Markov chain Monte Carlo and variational inference: Bridging the gap. [44]
  • [August 3] Pratik Sachdeva - Moreno-Bote, Rubén, et al. (2014). Information-limiting correlations. [45]
  • [July 26] Shariq Mobin - Karklin, Yan, and Eero P. Simoncelli (2011). Efficient coding of natural images with a population of noisy linear-nonlinear neurons.[46]
  • [July 20] Sean Mackesey - Buzsáki, György (2010). Neural syntax: cell assemblies, synapsembles, and readers. [47]
  • [July 13] Katarina Slama and Vasha DuTell - Akam, Thomas, and Dimitri M. Kullmann (2010). Oscillations and filtering networks support flexible routing of information. [48]
  • [July 6] Karl Zipser - Bojarski, Mariusz, et al (2016). End to End Learning for Self-Driving Cars. [49]
  • [June 29] Ian Robertson - Hermans, M., & Van Vaerenbergh, T. (2015). Towards Trainable Media: Using Waves for Neural Network-Style Training. [50]
  • [June 22] Group Discussion - Jonas, Eric, and Konrad Kording. (2016) Could a neuroscientist understand a microprocessor? [51]
  • [June 15] Spencer Kent, Mayur Mudigonda, and Eric Weiss - Kulkarni, Tejas D., et al (2015). Picture: A probabilistic programming language for scene perception. [52]
  • [June 8] Jesse Livezey - Bornschein, Jörg, Marc Henniges, and Jörg Lücke (2013). Are V1 simple cells optimized for visual occlusions? A comparative study. [53]
  • [June 1] Yubei Chen - Y. Karklin & M. S. Lewicki (2003). Learning higher-order structures in natural images. [54]
  • [May 25] Eric Dodds - Y. Karklin, C. Ekanadham, & E.P. Simoncelli (2012). Hierarchical spike coding of sound. [55]

Spring 2016

  • [May 11] NIPS exchange
  • [May 04] Chris Warner - MEJ Newman (2006). Finding Community Structure in Networks using the Eigenvectors of Matrices [56]
  • [Apr 27] Mr. (Alex) Anderson - E Ahissar, A Arieli (2012). Seeing Via Miniature Eye Movements- A Dynamic Hypothesis for Vision [57]
  • [Apr 20] Charles Frye, Ryan Zarcone, Brian Cheung - RM Neal, GE Hinton (1998). A View of the EM Algorithm That Justifies Incremental, Sparse, and Other Variants [58]
  • [Apr 13] Kohta Ishikawa - C Zetzsche, U Nuding (2009). Nonlinear and Higher-Order Approaches to the Encoding of Natural Scenes [59]
  • [Apr 06] Spencer Kent - IJ Goodfellow, J Pouget-Abadie, M Mirza, B Xu, D Warde-Farley, Y Bengio (2014). Generative Adversarial Nets [60]
  • [Mar 30] Guy Isley - R Chaudhuri, A Bernacchia, XJ Wang (2014). A Diversity of Localized Timescales in Network Activity [61]
  • [Mar 23] Brian Cheung - DJ Rezende, S Mohamed, I Danihelka, K Gregor, D Wierstra (2016). One-Shot Generalization in Deep Generative Models [62]
  • [Mar 16] Yubei Chen - A Beck, M Teboulle (2009). A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems [63]
  • [Mar 09] Sean Mackesey - P Fries (2015). Rhythms for Cognition: Communication Through Coherence [64]
  • [Mar 02] Charles Garfinkle - BB Ujfalussy, JK Makara, T Branco, M Lengyel (2015). Dendritic Nonlinearities are Tuned for Efficient Spike-Based Computations in Cortical Circuits [65]
  • [Feb 24] Eric Weiss - TS Lee, D Mumford (2003). Hierarchical Bayesian Inference in the Visual Cortex [66]
  • [Feb 17] Paxon Frady - C Eliasmith et al. (2012). A Large-Scale Model of the Functioning Brain [67]
  • [Feb 10] Cancelled - EECS Colloquium
  • [Feb 03] Charles Frye - L Aitchison, M Lengyel (2014). The Hamiltonian Brain [68]
  • [Jan 21] Jesse Livezy and Andrew Berger - S Shapero, M Zhu, J Hasler, C Rozell (2014). Optimal Sparse Approximations with Integrate and Fire Neurons [69]
  • [Jan 14] Daniel Toker - AK Seth, AB Barrett, L Barnett (2011). Causal Density and Integrated Information as Measures of Conscious Level [70]

Fall 2015

  • [Dec 17] Charles Frye - BM Lake, R Salakhutdinov, JB Tenenbaum (2015). Human-Level Concept Learning Through Probabilistic Program Induction [71]
  • [Dec 03] Omer Hazon - D Soudry, I Hubara, R Meir (2014). Expectation Backpropagation [72]
  • [Nov 26] Thanksgiving break
  • [Nov 19] Eric Dodds - EC Smith, MS Lewicki (2006). Efficient Auditory Coding [73]
  • [Nov 12] Vasha Dutell - H Hosoya, A Hyvarinen (2015). A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2 [74]

Summer 2014

  • [June 19] Buzsaki & Mizuseki (2014). The log-dynamic brain: how skewed distributions affect network operations. [75]
  • [June 12] Hukushima & Nemoto (1996). Exchange Monte Carlo method and application to spin glass simulations. [76]
  • [June 5] Shi & Griffiths (2009). Neural implementation of hierarchical bayesian inference by importance sampling. [77]
  • [May 29] Petersen & Crochet (2013). Synaptic computation and sensory processing in neocortical layer 2/3. [78]
  • [May 22] Laje R, Buonomano DV (2013) Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16:925-933 [79]

Spring 2014

  • [Jan 20] Sutskever 2012- Training Recurrent Neural Networks. [80]

Fall 2013

  • [Sep 18] Guillery & Sherman 2010 - Branched thalamic afferents: What are the messages that they relay to the cortex? [81]

Summer 2013

  • [July 10] Curto & Itskov 2008 - Cell Groups Reveal Structure of Stimulus Space [82]

Spring 2013

  • [Apr 8] Burak et al. 2009 - Accurate Path Integration in Continuous Attractor Network Models of Grid Cells [83] [84]
  • [Mar 27] Sreenivasan et al. 2011 - Grid cells generate an analog error-correcting code for singularly precise neural computation. [85]
  • [Mar 20] Killian et al. - A map of visual space in the primate entorhinal cortex [86]
  • [Mar 13] Doyle et al. 2011 - Architecture, constraints and behavior [87]
  • [Mar 6] Grady 2006 - Random Walks for Image Segmentation [88]
  • [Feb 27] Todorov 2012 - Parallels between sensory and motor information processing [89]
  • [Feb 13] Sohl-Dickstein 2012 - The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use [90]
  • [Feb 06] Girosi 1998 - An Equivalence Between Sparse Approximation and Support Vector Machines [91][92]
  • [Jan 30] Zipser et al. 1996 - Contextual Modulation in Primary Visual Cortex [93]

Ayzenshtat et al. 2012 - Population Response to Natural Images in the Primary Visual Cortex Encodes Local Stimulus Attributes and Perceptual Processing [94]

  • [Jan 23] Gillenwater et al. 2012 - Near-Optimal MAP Inference for Determinantal Point Processes [95] [96]
  • [Jan 08] Cathart-Harris et al. 2012 - Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin [97]

Fall 2012

  • [Aug 22] Maass et al. - Liquid State Computing [98] [99]
  • [Aug 29] Salakhutdinov & Hinton 2012 - An Efficient Learning Procedure for Deep Boltzmann Machines [100]
  • [Sep 05] Coates & Ng 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning [101]
  • [Sep 12] Quiroga 2012 - Concept Cells: The Building Blocks of Declarative Memory Functions [102]
  • [Oct 10] Moira & Bialek 2011 - Are Biological Systems Poised at Critcality? [103]
  • [Oct 17] Newman 2005 -Power laws, Pareto distributions and Zipfʼs law. [104]
  • [Nov 28] Todorov 2004 -Optimality Principles in Sensorimotor Control. [105]

Past TCN Papers

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