SPRING 2008
The 2008 edition of this course offers an advanced survey of selected topics of current interest in the neural and computational modeling of mammalian vision. This year's topics include brain imaging, visual search, brightness perception, and cortical anatomy. Some classes will be held at laboratories of nearby institutions. Students are expected to have a sufficient interdisciplinary grounding in the fundamentals of computational modeling of mammalian vision to read primary research sources extensively. A term project that combines a problem statement, literature review, and either (1) simulation of a model or (2) a design for a psychophysical experiment is required.
Answers to FREQUENTLY-ASKED QUESTIONS about CN810
Information for GUEST SPEAKERS
Dates of DELIVERABLES for student research reports
Click
on a date to go directly to a summary of that week's class, including assigned
readings. Links to guest speakers' home pages, weekly topics, and a list of
readings will also be found there, though these will be updated in real time
in the course of the semester.
Jan 17 Dae-Shik Kim -- field trip -- BU Med; see map
Jan 24 Tony Vladusich
Feb 7 Helen Barbas -- field trip -- Sargent College, BU
Feb 14 Arash Yazdanbakhsh -- 2:00 PM field trip -- Harvard Med; see map
Feb 21 Student presentations
Mar 6 Yury Petrov -- field trip -- Northeastern
Mar 13 Spring break
Apr 24 Student presentations, Room B03, 10:30 to 1:30
May 1 Student presentations, Room B03, 10:30 to 1:30
Jan 17 Dae-Shik Kim -- field trip -- BU Med; see map
The Center for Biomedical Imaging is located in the basement of the "Evans Research Center" building at
650 Albany Street
Boston, MA 02118
Once in the lobby of the building, please proceed to the third elevator from
the lobby, and press the button "BR" (basement rear) to get to the receptionist desk.
Readings
Ugurbil K, Toth L, Kim DS. How accurate is magnetic resonance imaging of brain function? Trends Neurosci. 2003 Feb;26(2):108-14. pdf
Roebroeck A, Galuske R, Formisano E, Chiry O, Bratzke H, Ronen I, Kim DS, Goebel R. High-resolution diffusion tensor imaging and tractography of the human optic chiasm at 9.4 T. Neuroimage. 2008 Jan 1;39(1):157-68. Epub 2007 Aug 24. pdf
Upadhyay J, Hallock K, Erb K, Kim DS, Ronen I. Diffusion properties of NAA in human corpus callosum as studied with diffusion tensor spectroscopy. Magn Reson Med. 2007 Nov;58(5):1045-53. pdf
Visual filling-in theories postulate that the brain uses contours to construct a ‘topograhic map’ of brightness (perceived increments) and darkness (perceived decrements). Here I cast a critical eye on the issue of topographic correspondence between neural activity and perception through the lens of several recent empirical studies, concluding that evidence for a strict correspondence is weak. I also re-assess another widespread assumption in visual science—namely, that brightness and darkness together form a one-dimensional perceptual space—providing evidence that brightness and darkness instead form (independent) perceptual dimensions.
Background Reading
Grossberg S, Todorovi? D (1988) Neural dynamics of 1-D and 2-D brightness perception: a unified model of classical and recent phenomena. Percept Psychophys 43: 241-277. PDF
Core Reading
Cornelissen FW, Wade AR, Vladusich T, Dougherty RF, Wandell BA (2006) No functional magnetic resonance imaging evidence for brightness and color filling-in in early human visual cortex. J Neurosci 26: 3634-3641. LINK
Vladusich T, Lucassen MP, Cornelissen FW (2006) Edge integration and the perception of brightness and darkness. J Vis 6: 1126-1147. LINK
Vladusich T, Lucassen MP, Cornelissen FW (2006) Do cortical neurons process luminance or contrast to encode surface properties? J Neurophysiol 95: 2638-2649. LINK
Supplementary Reading
Vladusich T, Lucassen MP, Cornelissen FW (2007) Brightness and darkness as perceptual dimensions. PLoS Comput Biol 3: e179. LINK
Jan 31 Jeremy Wolfe
The most current guided search ideas
Wolfe, J. M. (2007). Guided Search 4.0: Current Progress with a model of visual search. In W. Gray (Ed.), Integrated Models of Cognitive Systems (pp. 99-119). New York: Oxford. pdf
Shorter
Wolfe, J. M. (2003). Moving towards solutions to some enduring controversies in visual search. Trends Cogn Sci, 7(2), 70-76. pdf
See also:
http://www.scholarpedia.org/article/Visual_search
A short piece on basic features
Wolfe, J. M., & Horowitz, T. S. (2004). What attributes guide the deployment of visual attention and how do they do it? Nature Reviews Neuroscience, 5(6), 495-501. pdf
Current events
Short and already outdated
Wolfe, J. M., Horowitz, T. S., & Kenner, N. M. (2005). Rare items often missed in visual searches. Nature, 435, 439-440. pdf
Longer and more current
Wolfe, J. M., Horowitz , T. S., VanWert, M. J., Kenner, N. M., Place, S. S., & Kibbi, N. (2007). Low target prevalence is a stubborn source of errors in visual search tasks. JEP: General, 136(4), 623-638. pdf
Feb 7 Helen Barbas -- field trip -- Sargent College, BU
Barbas H and Zikopoulos B. The prefrontal cortex and flexible behavior.
Neuroscientist. 2007 Oct;13(5):532-45. pdf
Foreground
Qiu, F. T. and R. von der Heydt (2007). "Neural representation of
transparent overlay." Nat Neurosci 10(3): 283-4 pdf
Qiu FT, Sugihara T, von der Heydt R.
Figure-ground mechanisms provide structure for selective attention.
Nat Neurosci. 2007 Nov;10(11):1492-9 pdf
Hung CP, Ramsden BM, Roe AW.
A functional circuitry for edge-induced brightness perception.
Nat Neurosci. 2007 Sep;10(9):1185-90 pdf
(Grossbergian) pop-out
Yazdanbakhsh, A. and M. S. Livingstone (2006). "End stopping in V1 is
sensitive to contrast." Nat Neurosci 9(5): 697-702 pdf
Background
Grossberg, S. and A. Yazdanbakhsh (2005). "Laminar cortical dynamics of
3D surface perception: stratification, transparency, and neon color
spreading." Vision Res 45(13): 1725-43 pdf
Cornelissen, F. W., A. R. Wade, et al. (2006). "No functional magnetic
resonance imaging evidence for brightness and color filling-in in early
human visual cortex." J Neurosci 26(14): 3634-41 pdf
Deep background
Grossberg, S. and E. Mingolla (1985). "Neural dynamics of form
perception: boundary completion, illusory figures, and neon color
spreading." Psychol Rev 92(2): 173-211 pdf
Rossi AF, Paradiso MA. Neural Correlates of Perceived Brightness in
the Retina, Lateral Geniculate Nucleus, and Striate Cortex.
J Neurosci. 1999 Jul 15;19(14):6145-56. pdf
Feb 28 Moshe Bar
M. Bar (2007). The Proactive Brain: Using analogies and associations to generate predictions. Trends in Cognitive Sciences, 11(7), 280-289. http://barlab.mgh.harvard.edu/papers/TICS2007.pdf
M. Bar, K.S. Kassam, A.S. Ghuman, J. Boshyan, A.M. Schmidt, A.M. Dale, M.S. Hamalainen, K. Marinkovic, D.L. Schacter, B.R. Rosen, & E. Halgren (2006). Top-down facilitation of visual recognition. Proceedings of the National Academy of Science, 103(2), 449-454. http://barlab.mgh.harvard.edu/papers/PNAS2006.pdf
M. Bar, E. Aminoff, M. Mason, & M. Fenske (2007). The units of thought. Hippocampus, 17(6), 420-428.
http://barlab.mgh.harvard.edu/papers/Hippo07.pdf
Mar 6 Yury Petrov -- field trip -- Northeastern
S. Baillet, J.C. Mosher, & R.M. Leahy (2001). IEEE Signal Processing Magazine, 18(6), 14-30. pdf
S. Thorpe, F. Fize, & C. Marlot (1996). Speed of processing in the human visual system. Nature, 381, 520-522. pdf
A. Skoczenski, & A. Norcia (1998). Neural Noise Limitations on Infant Visual Sensitivity. Nature, 391, 697-700. pdf
L. G. Appelbaum, A. R. Wade, V. Y. Vildavski, M. W. Pettet, & A. M. Norcia (2006). Cue-invariant networks for figure and background processing in human visual cortex. Journal of Neuroscience, 26(45), 11695-1170. pdf
Mar 13 Spring break
Mar 20 Chris Pack
Core readings
Pack, C.C. and Born, R.T. (2001) Temporal dynamics of a neural solution to the aperture problem in visual area MT of macaque brain. Nature, 409, 1040-1042.
http://apps.mni.mcgill.ca/research/cpack/pack_born2001.pdf
Pack, C.C., Livingstone, M.S., Duffy, K.R., and Born, R.T. (2003) End-stopping and the aperture problem: Two-dimensional motion signals in macaque V1. Neuron, 39, 671-680.
http://apps.mni.mcgill.ca/research/cpack/packlivdufborn03.pdf
Pack, C.C., Conway, B.R., Born, R.T., and Livingstone, M.S. (2006) Spatiotemporal structure of nonlinear subunits in macaque visual cortex. Journal of Neuroscience, 26, 893-907.
http://apps.mni.mcgill.ca/research/cpack/packconwaybornlivingstone06.pdf
Background re: Reverse correlation (for 2nd half of class)
For mathematical treatment:
C hapter 2 of Peter Dayan and L. F. Abbott
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. Cambridge, MA MIT Press, 2001.
Historical overview:
Ringach DL. Mapping receptive fields in primary visual cortex. J Physiol. 2004 Aug 1;558(Pt 3):717-28.
http://www.ncbi.nlm.nih.gov/pubmed/15155794
Mar 27 Steve Grossberg
Ellias, S.A. and Grossberg, S. (1975). Pattern formation, contrast control, and oscillations in the short-term memory of shunting on-center off-surround networks. Biological Cybernetics, 20, 69-98. Available in PDF (EllGro1975BiolCyb.pdf)(1,923Kb).
Grossberg, S.(1993). A solution of the figure-ground problem for biological vision. Neural Networks, 6, 463-483. Available in PDF (Gro1993NN.pdf) (562Kb).
Grossberg, S. and Rudd, M.E. (1992). Cortical dynamics of visual motion perception: Short-range and long-range apparent motion (with M.E. Rudd). Psychological Review , 99 , 78-121. Available in PDF (GroRud1992PsyRev.pdf) (12,058Kb).
Apr 3 Antonio Torralba
with additional brief presentations by Tren Huang, Praveen Pilly, and Adam Reeves
and panel discussion including Elliot Saltzman and Tony Vladusich, as well as "all of the above"
Readings for Antonio's presentation:
1) The role of context in object recognition
A. Oliva, A. Torralba
Trends in Cognitive Sciences, vol. 11(12), pp. 520-527. December 2007.
http://cvcl.mit.edu/Papers/OlivaTorralbaTICS2007.pdf
2) Object Recognition by Scene Alignment
B. C. Russell, A. Torralba, C. Liu, R. Fergus, W. T. Freeman.
Advances in Neural Information Processing Systems, 2007.
http://people.csail.mit.edu/torralba/publications/nipsRecognitionBySceneAlignment.pdf
Also, Antonio will discuss recent trends in computer vision using very large datasets for learning
See the following site for additional links to longer papers and an overview of very large datasets:
http://people.csail.mit.edu/torralba/tinyimages/
Follow-up for Tren's presentation:
Grossberg, S. and Huang, T.-R. (2008) ARTSCENE: A Neural System for Natural Scene Classification. Journal of Vision, in press.
http://cns.bu.edu/~steve/GroHuang2008JOV.pdf [Note: "follow-up" means that your do not need to read this for background for Tren's presentation, but may consult it for details in the future.]
Follow-up for Praveen's presentation:
Grossberg, S. and Pilly, P. (2008) Temporal dynamics of decision-making during motion perception in the visual cortex. /Vision Research/, in press. http://cns.bu.edu/~steve/GroPilly2008DecisionMakingVR [Note: "follow-up" means that your do not need to read this for background for Praveen's presentation, but may consult it for details in the future.]
Backround for Adam's presentation:
Tutorial: "Foundations of Vision" Sinauer, 1995, explains the use of matrices in color vision (in Chapter 9)
and many of the basic facts of color in a clear way. The CNS Library has a copy of this book.
Core reading for Adam's presentation:
Brainard, D. H., Longère, P., Delahunt, P. B., Freeman, W. T., Kraft, J. M., & Xiao, B. (2006). Bayesian model of human color constancy. Journal of Vision, 6(11):10, 1267-1281, http://journalofvision.org/6/11/10/, doi:10.1167/6.11.10.
http://journalofvision.org/6/11/10/Brainard-2006-jov-6-11-10.pdf [Note: Read for gist before class.]
Elliot suggests the following "conversation starter" for the panel discussion:
Warren, W.H. (2005) Direct Perception: The View from Here. Philosophical Topics, 33(1), 335-361. [Note: Don't shoot the messenger.] pdf
Apr 10 George Alvarez
Ensemble Visual Features: Efficient Codes that can be Represented with Reduced Attention
Readings can be downloaded from:
http://cvcl.mit.edu/george/assets/transfer/AlvarezReadings.zip
Topics:
(1) a working definition of attention
(2) adefinition of ensemble visual features, which capture higher-order structure in an image, and
(3) how these ensemble features appear to be represented accurately even
with reduced attention. I will explain why I think these ensemble features
are efficient codes that can be represented with reduced attention,
and propose the possibility that there are "natural ensembles" which
are encoded particularly efficiently. We'll also discuss how these
concepts of ensemble features and efficient codes make links between
visual perception, visual attention, and neural models of visual
coding.
Unfortunately, only one of my papers on ensemble features is in press
(the others are in prep). So when you read Alvarez & Oliva,
(in press), please keep in mind that particular study should be
considered a "proof of principle" with a simple ensemble feature, and
that my class presentation will show how we've started to scale up to
more complex visual features.
3 Core Readings
A representative publication showing how I think about attention (the
key point is that the quality of representation decreases as you
attend to more things)…
1) Franconeri, S., Alvarez, G. A., & Enns, J. (in press). How many
locations can you select at once? Journal of Experimental Psychology:
Human Perception and Performance.
A controlled "proof of principle", showing that a simple ensemble
feature can be represented outside the focus of attention…
2) Alvarez, G. A., & Oliva, A. (in press). The representation of simple
ensemble features outside the focus of attention. Psychological
Science.
Modeling work laying the ground work for my current research looking
at the representation of more complex, naturalistic ensembles with
reduced attention (I don't have the paper ready yet, but I will
present that work during class)…
3) Torralba, A., Oliva, A., Castelhano, M., & Henderson, J.M. (2006).
Contextual guidance of eye movements and attention in real-world
scenes: the role of global features in object search. Psychological
Review, 113, 766-786.
Supplemental Readings
These readings cover some work on natural image statistics, efficient
visual codes, and in some cases their relation to the response
properties of visual neurons. The relevance to work on attention is
that these efficient codes might also be robust to increases in noise,
and thus more robust to the withdrawal of attention.
If you only read a couple, or if you want to start with something
comprehensible, I recommend reading the Olshausen and Field papers
first.
I include the Ruderman paper to represent another perspective on the
same issues, but it's quite dense, and I can't explain it, so consider
it supplemental, supplemental reading.
I also recommend the work of Dale Purves and colleagues for examples
of work directly relating natural image statistics to the types of
perceptual phenomena that might interest this group (e.g., perceived
hue, saturation, brightness, some perceptual illusions, etc.)
http://www.purveslab.net/publications/
Olshausen, B. A., & Field, D. J. (1996). Natural image statistics and
efficient coding. Network, 7(2), 333-339.
Olshausen, B. A., & Field, D. J. (1997). Sparse coding with an
overcomplete basis set: a strategy employed by V1? Vision Res, 37(23),
3311-3325.
Olshausen, B. A., & Field, D. J. (2000). Vision and the coding of
natural images. American Scientist, 88, 238-245.
Field, D. J. (1994). What is the goal of sensory coding? Neural
Computation, 6, 559-601.
Geisler, W. S. (2008). Visual perception and the statistical
properties of natural scenes. Annual Review of Psychology, 59,
167-192.
Graham, D. J., & Field, D. J. (2007). Efficient coding of natural
images. In L. R. Squire (Ed.), New Encyclopedia of Neuroscience:
Elsevier.
Ruderman, D. L. (1994). The statistics of natural images. Network:
Computation in Neural Systems, 5, 517-548.
This page is maintained by Ennio Mingolla
Please direct questions to: ennio @ cns.bu.edu
