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dc.contributor.advisorBoyden, Edward S.
dc.contributor.authorÇeliker, Orhan Tunç
dc.date.accessioned2022-06-15T13:12:49Z
dc.date.available2022-06-15T13:12:49Z
dc.date.issued2022-02
dc.date.submitted2022-03-04T20:47:40.841Z
dc.identifier.urihttps://hdl.handle.net/1721.1/143327
dc.description.abstractThe nematode C. elegans, a transparent animal with 302 neurons, is a suitable model organism for whole-brain measurement of nervous activity. However, under panneuronal labeling, it is difficult to resolve the identity of the neurons by shape or location alone. We propose a fluorescent in situ hybridization (FISH) based pipeline for reading out gene expression from neurons. Using optimization methods, we select a compact set of genes that provide enough information to distinguish every neighboring pair of neurons in the nervous system. We show that we can process volumetric images of live and fixed C. elegans to read out the gene expression patterns of each observed neuron and match it to their calcium indicator data. Separately, we also outline computational approaches to processing fluorescence data from novel fluorescent sensors.
dc.publisherMassachusetts Institute of Technology
dc.rightsIn Copyright - Educational Use Permitted
dc.rightsCopyright MIT
dc.rights.urihttp://rightsstatements.org/page/InC-EDU/1.0/
dc.titleFast image and data processing methods for novel neuroscience technologies
dc.typeThesis
dc.description.degreePh.D.
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
mit.thesis.degreeDoctoral
thesis.degree.nameDoctor of Philosophy


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