[talks] Nicholas Turner will present his generals exam on Monday, May 16, 2016 at 2pm in CS 402

[Nicki Gotsis]

Nicholas Turner will present his generals exam on Monday, May 16, 2016 at 2pm in CS 402.

The members of his committee are Sebastian Seung (adviser), Tom Funkhouser, and Jianxiong Xiao

Everyone is invited to attend his talk, and those faculty wishing to remain for the oral exam following are welcome to do so.  His abstract and reading list follow below.

*Abstract:*
Connectomics studies neural circuits by extracting their structural
properties from electron microscopy (EM) images. Current state-of-the-art
approaches use convolutional neural networks as a step towards segmenting
aligned stacks of EM images. One application of these methods attempts to
study the mouse retina. However, current practices have featured poor
performance in describing the geometry of ganglion cell somata, which are
difficult to segment as they can feature large image staining gaps.

I will present work addressing two related challenges. First, I will
demonstrate how we can preprocess the image stack to improve the
segmentation accuracy of current approaches. These improvements allow us to
quantify basic properties of ganglion cell somata. Second, I will present
preliminary work motivated by these results to provide segmentation across
multiple layers of tissue by means of a single multiscale model.

*Reading List:*
Bishop, Christopher M. "Pattern Recognition." *Machine Learning* (2006).

Ian Goodfellow, Yoshua Bengio, and Aaron Courville. "Deep Learning." Book
in preparation for MIT Press, 2016.

J. S. Kim, M. J. Greene, A. Zlateski, K. Lee, M. Richardson, S. C. Turaga,
M. Purcaro, M. Balkam, A. Robinson, B. F. Behabadi, M. Campos, W. Denk, H.
S. Seung, and EyeWirers. Space-time wiring specificity supports direction
selectivity in the retina. <http://www.ncbi.nlm.nih.gov/pubmed/24805243>
*Nature**509*, 331-6 (2014).

Sanes, Joshua R., and Richard H. Masland. "The types of retinal ganglion
cells: current status and implications for neuronal classification." *Annual
review of neuroscience* 38 (2015): 221-246.

Ciresan, Dan, et al. "Deep neural networks segment neuronal membranes in
electron microscopy images." *Advances in neural information processing
systems*. 2012.

S. C. Turaga, K. L. Briggman, M. Helmstaedter, W. Denk, and H. S.
Seung. *Maximin
affinity learning of image segmentation*. CoRR. abs/0911.5372 (2009).

S. C. Turaga, J. F. Murray, V. Jain, F. Roth, M. Helmstaedter, K. Briggman,
W. Denk, and H. S. Seung. Convolutional networks can learn to generate
affinity graphs for image segmentation.
<http://www.ncbi.nlm.nih.gov/pubmed/19922289>*Neural Comput.* *22*, 511-38
(2010).

Glorot, Xavier, and Yoshua Bengio. “Understanding the difficulty of
training deep feedforward neural networks.”
<http://jmlr.csail.mit.edu/proceedings/papers/v9/glorot10a/glorot10a.pdf>
*Proceedings
of the International Conference on Artificial Intelligence and Statistics
(AISTATS’10). Society for Artificial Intelligence and Statistics*. 2010.

Bengio, Yoshua. "Learning deep architectures for AI." *Foundations and
trends® in Machine Learning* 2.1 (2009): 1-127.

I. Arganda-Carreras, S.C. Turaga, D.R. Berger, D. Cireşan, A. Giusti, L.M.
Gambardella, J. Schimdhuber, D. Laptev, S. Dwivedi, J.M. Buhmann, T. Liu,
M. Seyedhosseini, T. Tasdizen, L. Kamentsky, R. Burget, V. Uher, X. Tan, C.
Sun, T.D. Pham, E. Bas, M.G. Uzunbas, A. Cardona, J. Schindelin, H.S.
Seung. Crowdsourcing the creation of image segmentation algorithms for
connectomics
<http://journal.frontiersin.org/article/10.3389/fnana.2015.00142/abstract>.
Front Neuroanat *142*, (2015).