Show simple item record

dc.contributor.advisorEdward S. Boyden.en_US
dc.contributor.authorYoon, Young Gyuen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2018-09-17T15:57:31Z
dc.date.available2018-09-17T15:57:31Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/118100
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 181-189).en_US
dc.description.abstractNeuroscience always has been a heavily technology-starved field that has been often revolutionized with the rise of a new technology; and arguably the future advancement of neuroscience will also depend largely on the development of new technologies that allow acquiring new data sets that can provide deeper insights into the brain. While there is no universal agreement as to what data sets are needed to fully reveal the underlying principles of brain computation, neurons will certainly serve as an important layer to study the brain considering their discreteness and electrical characteristics. In other words, seeing a brain as a circuitry that is made of the neurons, it is important to study the relation between the brain's structure and its function at the neuronal level. Unfortunately, a brain is a huge network that consists of a huge number of neurons which makes it difficult to see the "big picture" while retaining the single neuron resolution. The aim of this study is to the develop such technologies that allow to see and analyze the large network's structure and dynamics with the single neuron resolution. The core strategy is to use optical microscopy to acquire the raw data and to infer the information of our interests with computational techniques with designing both optical and computational parts with each other in mind to maximize the synergy. The first part of the thesis describes the development and application of computational imaging techniques to monitor the brain activity in 3-D which allowed us to see how the neurons interact at an unprecedented speed. The second part describes a computational approach to extract a wiring diagram of a brain from an optical image of the brain rather than an electron microscopy image, as optical microscopy is undergoing rapid development and is likely to outperform electron microscopy in terms of scalability which will be an important criterion to map the whole brain. These will be the tools of great utility in neuroscience that can generate rich data sets that will be of wide interests to system neuroscientists as well as cellular/molecular neuroscientists.en_US
dc.description.statementofresponsibilityby Young Gyu Yoon.en_US
dc.format.extent189 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleOptical and computational approaches for mapping brain activity and structureen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.identifier.oclc1052124209en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record