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Supplementary Materials
Recommended Papers
By Kristin Koch, Judith
McLean, Ronen Segev, Michael A. Freed, Michael J. Berry, Vijay
Balasubramanian, and Peter Sterling.
In class we talked briefly about the information capacity of the
optic nerve and the degree to which the information computed by the
retina is compressed or filtered. This paper discusses the retina
in information-theoretic terms.
By Lettvin, J. Y.,
Maturana, H. R., McCulloch, W. S. and Pitts, W. H. (1959) Proceedings
of the Institute for Radio Engineers, Volume 47:1940-1951.
The title of the paper by Koch et al. is a play on the title
of this classic paper. The authors are considered to be pioneers
in neurophysiology and the publication is one of the most cited in the
field.
By Zucker, S. W. In J. Leo van Hemmen and T. J. Sejnowski (eds.),
Problems in Systems Neuroscience, Oxford University Press, 2001.
Stu Geman put Steve Zucker's work on geometric interpretations
of the computations performed by the visual cortex on his list of
top computational neuroscience papers. Here is one of Zucker's
better known papers; there are others on his Yale web site.
By John von Neumann. Yale University
Press. New Haven, CT. 1958. Second Edition, 2000.
On the first day of classes I mentioned that both Alan Turing and
John von Neumann wrote about the connections between brains and
computers. This book contains the text that was to be John von
Neumann's Silliman Lectures at Yale University in 1958 had he not
died of cancer in 1957. The material is still fresh and insightful
despite how little was known about the brain at the time.
Tutorials and Overviews
Usually an area has a collection of seminal papers or, if
it is well established, one or more overview papers that
everyone agrees are worth reading. If you're interested in a
topic, try to find such a paper and read it.
Invariant Features
How Does Our Visual System Achieve Shift and Size Invariance?
By Laurenz Wiskott
In: Problems in Systems Neuroscience. J. L. van Hemmen and
T. J. Sejnowski (eds), Oxford University Press (2003).
Population Coding
Neural correlations, population coding and computation
By Bruno B. Averbeck, Peter E. Latham and Alexandre Pouget
Nature Reviews Neuroscience. 7:358-366 (May 2006).
(You can probably get this electronically through the Stanford library.)
Perceptual Grouping
Perceptual grouping in space and in space-time:
An exercise in phenomenological psychophysics.
By Kubovy, M. and Gepshtein, S.
In: Perceptual Organization in Vision: Behavioral and Neural
Perspectives. Behrmann, M. and Kimchi, R. and Olson, C. R.
(eds), Lawrence Erlbaum (2003).
Related Workshops
Workshops are particularly useful to find tutorials, researchers
working in the area, and the topics driving current research.
Simon Lucey and Tsuhan Chen (Co-Chairs)
Konrad Koerding and Bruno Olshausen (Co-Chairs)
Alexandre Pouget, Richard Zemel and Peter Dayan (Co-Chairs)
See the recommended review paper and other relevant publications.
Institutes and Programs
Another good source of papers and material of a tutorial nature.
This is a convenient source of information covering much of the
visual system. In particular it is the source
of the data I recounted in the second class regarding the
activity in the different visual areas over time in response to a
visual stimulus. Contrary to what I might have said, the
ordinate (or y axis) measures the percentage of cells that
have begun to respond to the stimulus and the abscissa (or x
axis) is time from the stimulus onset measured in milliseconds.
Miscellaneous Links
I will add links as they accumulate.
Please send pointers to interesting and relevant web sites.
Here is an interesting Berkeley
news
release featuring the
paper
on reconstructing natural scenes from recordings of the lateral geniculate nuclei by
Garrett Stanley,
Fei-Fei Li and
Yang Dan.
Pages from Laurent Itti's lab presenting neuromorphic models and results
from experiments predicting the behavior of human subjects on various
psychophysical tasks
The current content is pretty sparse, but if we get into this further
I'll try to beef it up a bit.
Christopher Bishop's Netlab software
This Matlab library includes functions implementing
probabilistic principal components analysis (PPCA), radial basis
function (RBF) networks, Kohonen's self-organizing maps (SOMs),
expectation maximization (EM) for mixtures of Gaussians and generative
topographic maps (GTM) (which are touted to be a more principled
alternative to SOMs), and much more.
Available temporarily on the web until November
Note that this representation doesn't address the problem
that I alluded to in class, namely that our current
understanding of V1 has been biased by our choice of test
stimuli; the chapter "What is the other 85% of V1 doing?" (Olshausen and Field, 2004) (full citation in References) appears to address that issue.
Graphics in the slides are important to understanding the work.
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