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).