[talks] Sachin Ravi will present his general exam on Monday, May 16, 2016 at 10am in CS 302

[Nicki Gotsis]

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

Abstract

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.

Reading List

[1] 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

[2] 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 
systems.

[3] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. Imagenet 
classification with deep convolutional neural networks. In Advances in 
Neural Information Processing Systems, page 2012

[4] 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

[5] 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

[6] 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

[7] Cohen, T.S. and Welling, M., 2016. Group Equivariant Convolutional 
Networks. arXiv preprint arXiv:1602.07576.

[8] Dieleman, Sander, Jeffrey De Fauw, and Koray Kavukcuoglu. Exploiting 
Cyclic Symmetry in Convolutional Neural Networks. arXiv preprint 
arXiv:1602.02660 (2016).

[9] 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.

[10] Jaderberg, M., Simonyan, K. and Zisserman, A., 2015. Spatial 
transformer networks. In Advances in Neural Information Processing 
Systems (pp. 2008-2016).
Vancouver

[11] Yoshua Bengio Ian Goodfellow and Aaron Courville. Deep learning. 
Book in preparation for MIT Press, 2016.

[12] Bishop, Christopher M. Pattern Recognition. Machine Learning (2006).