[Ml-stat-talks] Dr. Sebastian Seung Presents "The Computational Challenges of Connectomics"

David Blei blei at CS.Princeton.EDU
Wed Mar 14 06:59:42 EDT 2012


*Friday, March 16*
 *12:00pm*
*0-S-6 Green Hall*

*“The Computational Challenges of Connectomics”*

*[image: Description: cid:image005.png at 01CD004D.0CB73EF0]*
** **
According to a doctrine known as connectionism, brain function and
dysfunction depend primarily on patterns of connectivity between neurons.
Connectionism has been explored theoretically with mathematical models of
neural networks since the 1940s. It has proved difficult to test these
models through activity measurements alone. For conclusive empirical tests,
information about neural connectivity is also necessary, and could be
provided by new imaging methods based on serial electron microscopy. The
bottleneck in using these new methods is now shifting to the data analysis
problem of extracting neural connectivity from the images. New multibeam
scanning electron microscopes will soon generate a petabyte of image data
from a cubic millimeter of brain tissue every two weeks.  From such images,
it should be possible to map every connection between neurons in the
volume---in principle.  Unfortunately, it could take up to a million years
for a single person to carry out this feat manually.   Clearly, our
capacity to acquire "big data" from the brain has far outpaced our ability
to analyze it.  My lab has been developing computational technologies to
deal with this data deluge.  These include image analysis by artificial
intelligence (AI) based on machine learning, and methods of "crowdsourcing"
image analysis to human intelligence.  Our technologies have been
implemented in eyewire.org, an online community that mobilizes laypersons
to help map neural connections of the retina by interacting with AI.
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