[talks] Sachin Ravi will present his general exam on Monday, May 16, 2016 at 10am in CS 302
ngotsis at CS.Princeton.EDU
Mon May 9 11:04:10 EDT 2016
Sachin Ravi will present his general exam on Monday, May 16, 2016 at 10am in CS 302.
The members of his committee are Kai Li (adviser), Sebastian Seung, and Tom Funkhouser.
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.
Title: Automated synapse detection in EM images
The field of Connectomics involves mapping the network structure of
neural circuits. Currently, the most promising way to obtain such maps
is by automated reconstruction of neural circuits by analyzing 3D
electron microscopic (EM) brain images. An important task in this
reconstruction is the localization of synapses, the mechanism through
which neurons pass electrical or chemical signals to each other. In
addition to localization, it is important to know the source and
destination of the signal (or polarity of the synapse) in order to
derive the true connectivity of the network.
In this work, we present an automated method to detect synapses using
convolutional networks and evaluate the method on a mouse cortex
dataset. Additionally, we discuss a method to achieve equivariance in
convolutional networks to a finite group of rotations and reflections.
Encoding equivariance in the network makes more efficient use of
parameters and could potentially benefit synapse detection and polarity
prediction because of the rotational invariance present in EM images.
 Yann A LeCun, L´eon Bottou, Genevieve B Orr, and Klaus-Robert
M¨uller. Efficient backprop. In Neural networks: Tricks of the trade,
pages 9–48. Springer, 2012
 Le Cun, B.B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W.
and Jackel, L.D., 1990. Handwritten digit recognition with a
back-propagation network. In Advances in neural information processing
 Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. Imagenet
classification with deep convolutional neural networks. In Advances in
Neural Information Processing Systems, page 2012
 Kisuk Lee, Aleksandar Zlateski, Ashwin Vishwanathan, and H.
Sebastian Seung. Recursive training of 2d-3d convolutional networks for
neuronal boundary detection. CoRR, abs/1508.04843, 2015
 Dan C Cire¸san, Alessandro Giusti, Luca M Gambardella, and J¨urgen
Schmidhuber. Mitosis detection in breast cancer histology images with
deep neural networks. In Medical Image Computing and Computer-Assisted
Intervention–MICCAI 2013, pages 411–418. Springer, 2013
 C. J. Becker, K. Ali, G. Knott and P. Fua. Learning Context Cues for
Synapse Segmentation, in IEEE Transactions on Medical Imaging, vol. 32,
num. 10, p. 1864--1877, 2013
 Cohen, T.S. and Welling, M., 2016. Group Equivariant Convolutional
Networks. arXiv preprint arXiv:1602.07576.
 Dieleman, Sander, Jeffrey De Fauw, and Koray Kavukcuoglu. Exploiting
Cyclic Symmetry in Convolutional Neural Networks. arXiv preprint
 Osadchy, M., Cun, Y.L. and Miller, M.L., 2007. Synergistic face
detection and pose estimation with energy-based models. The Journal of
Machine Learning Research, 8, pp.1197-1215.
 Jaderberg, M., Simonyan, K. and Zisserman, A., 2015. Spatial
transformer networks. In Advances in Neural Information Processing
Systems (pp. 2008-2016).
 Yoshua Bengio Ian Goodfellow and Aaron Courville. Deep learning.
Book in preparation for MIT Press, 2016.
 Bishop, Christopher M. Pattern Recognition. Machine Learning (2006).
More information about the talks