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<div><font face="Tahoma"><span style="font-size: 13.3333px;" data-mce-style="font-size: 13.3333px;">Thomas Macrina will present his General Exam on Monday, October 22, 2018 at 2pm in PNI 159.</span><br data-mce-bogus="1"></font></div><div><font face="Tahoma"><span style="font-size: 13.3333px;" data-mce-style="font-size: 13.3333px;"><br data-mce-bogus="1"></span></font></div><div><font face="Tahoma"><span style="font-size: 13.3333px;" data-mce-style="font-size: 13.3333px;">The members of his committee are as follows: </span></font><font face="Tahoma"><span style="font-size: 13.3333px;" data-mce-style="font-size: 13.3333px;">Sebastian Seung (adviser), Jonathan Pillow (Neuro), and Barbara Engelhardt.</span></font><br>
</div><div><font face="Tahoma"><span style="font-size: 13.3333px;" data-mce-style="font-size: 13.3333px;"><br data-mce-bogus="1"></span></font></div><div><font face="Tahoma"><span style="font-size: 13.3333px;" data-mce-style="font-size: 13.3333px;">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.</span><br data-mce-bogus="1"></font></div>
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<div style="color: #000000; font-family: Tahoma; font-size: 10pt;" data-mce-style="color: #000000; font-family: Tahoma; font-size: 10pt;">Aligning petascale serial-section electron microscopy images, and analyzing structure + function of networks of real neurons</div>
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<div style="color: #000000; font-family: Tahoma; font-size: 10pt;" data-mce-style="color: #000000; font-family: Tahoma; font-size: 10pt;"><i>Abstract</i><br>
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<div style="color: #000000; font-family: Tahoma; font-size: 10pt;" data-mce-style="color: #000000; font-family: Tahoma; font-size: 10pt;">Routinely reconstructing neural wiring diagrams based on petascale serial section electron microscopy would be a boon for neuroscience. The major obstacle is producing a low-error alignment of 2D images into a 3D stack. I will discuss how we have used
elastic models based on normalized cross correlation correspondences, and now optical flow models based on deep learning to overcome this obstacle. With routine reconstructions on the horizon, focus can turn to analysis of these wiring diagrams. I will also
discuss my work investigating these real neural networks: correlating them with neural activity, and using contacts between neurons as a basis for connection properties<i>.</i></div>
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<div style="color: #000000; font-family: Tahoma; font-size: 10pt;" data-mce-style="color: #000000; font-family: Tahoma; font-size: 10pt;"><i>Reading List</i></div>
<div style="color: #000000; font-family: Tahoma; font-size: 10pt;" data-mce-style="color: #000000; font-family: Tahoma; font-size: 10pt;">Braitenberg, Valentino, and Almut Schuez. 1998. Cortex Statistics Geometry Neuronal Connectivity. Springer.<br>
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning (Adaptive Computation and Machine Learning). Edited by Francis Bach. The MIT Press.<br>
Jaderberg, Max, Karen Simonyan, Andrew Zisserman, and Koray Kavukcuoglu. 2015. “Spatial Transformer Networks.” In Advances in Neural Information Processing Systems 28, edited by C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, 2017–25. Curran
Associates, Inc.<br>
Kasthuri, Narayanan, Kenneth Jeffrey Hayworth, Daniel Raimund Berger, Richard Lee Schalek, José Angel Conchello, Seymour Knowles-Barley, Dongil Lee, et al. 2015. “Saturated Reconstruction of a Volume of Neocortex.” Cell 162 (3): 648–61.<br>
Kubota, Yoshiyuki. 2014. “Untangling GABAergic Wiring in the Cortical Microcircuit.” Current Opinion in Neurobiology 26 (June): 7–14.<br>
Lee, Wei-Chung Allen, Vincent Bonin, Michael Reed, Brett J. Graham, Greg Hood, Katie Glattfelder, and R. Clay Reid. 2016. “Anatomy and Function of an Excitatory Network in the Visual Cortex.” Nature 532 (7599): 370–74.<br>
Luo, Liqun. 2015. Principles of Neurobiology. 1 edition. Garland Science.<br>
Mishchenko, Yuriy, Tao Hu, Josef Spacek, John Mendenhall, Kristen M. Harris, and Dmitri B. Chklovskii. 2010. “Ultrastructural Analysis of Hippocampal Neuropil from the Connectomics Perspective.” Neuron 67 (6): 1009–20.<br>
Ranjan, Anurag, and Michael J. Black. 2016. “Optical Flow Estimation Using a Spatial Pyramid Network.” arXiv [cs.CV]. arXiv. http://arxiv.org/abs/1611.00850.<br>
Saalfeld, Stephan, Richard Fetter, Albert Cardona, and Pavel Tomancak. 2012. “Elastic Volume Reconstruction from Series of Ultra-Thin Microscopy Sections.” Nature Methods 9 (7): 717–20.<br>
Yoo, I., D. G. C. Hildebrand, W. F. Tobin, and W. C. A. Lee. 2017. “ssEMnet: Serial-Section Electron Microscopy Image Registration Using a Spatial Transformer Network with Learned Features.” Deep Learning in. https://link.springer.com/chapter/10.1007/978-3-319-67558-9_29.<br>
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