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dc.contributor.advisorPfister, Hanspeter
dc.contributor.authorGonda, Felix Emmanuel
dc.date.accessioned2021-07-13T06:44:46Z
dc.date.created2021
dc.date.issued2021-05-14
dc.date.submitted2021-05
dc.identifier.citationGonda, Felix Emmanuel. 2021. Efficient Reconstruction and Proofreading of Neural Circuits. Doctoral dissertation, Harvard University Graduate School of Arts and Sciences.
dc.identifier.other28499488
dc.identifier.urihttps://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37368462*
dc.description.abstractThis thesis presents the design and implementation of software technologies for neural circuit reconstruction in light of a need for radical improvements in efficiency and scalability. It focuses primarily on reconstructing the anatomical structures and connections of neurons in electron microscopy datasets of brains and extends to other problem domains, including object analysis in still images and videos. Its main contributions include a robust mechanism for training complex segmentation algorithms, optimization of 3D convolutions for efficient processing of volumetric data and videos, design of recurrent neural networks for accurate reconstruction of 3D neurons, and an analytics framework for the automatic discerning of potential errors in segmentation and fast proofreading neural sub-graphs. Implementing these technologies has contributed to new usable tools strategically targeted towards the efficient reconstruction of neural circuits.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dash.licenseLAA
dc.subjectBidirectional active learning
dc.subjectConnectomics
dc.subjectConvolutional LSTM
dc.subjectNeural Circuits
dc.subjectNeural proofreading
dc.subjectNeuron segmentation
dc.subjectComputer science
dc.titleEfficient Reconstruction and Proofreading of Neural Circuits
dc.typeThesis or Dissertation
dash.depositing.authorGonda, Felix Emmanuel
dc.date.available2021-07-13T06:44:46Z
thesis.degree.date2021
thesis.degree.grantorHarvard University Graduate School of Arts and Sciences
thesis.degree.levelDoctoral
thesis.degree.namePh.D.
dc.contributor.committeeMemberGlassman, Elena
dc.contributor.committeeMemberTompkin, James
dc.type.materialtext
thesis.degree.departmentEngineering and Applied Sciences - Computer Science
dc.identifier.orcid0000-0003-1870-0905
dash.author.emailfelix.e.gonda@gmail.com


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