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dc.contributor.advisorShea-Brown, Eric
dc.contributor.authorHarris, Kameron Decker
dc.date.accessioned2018-01-20T00:58:05Z
dc.date.available2018-01-20T00:58:05Z
dc.date.submitted2017
dc.identifier.otherHarris_washington_0250E_18044.pdf
dc.identifier.urihttp://hdl.handle.net/1773/40831
dc.descriptionThesis (Ph.D.)--University of Washington, 2017
dc.description.abstractAt first glance, the neuronal network seems like a tangled web in many areas throughout the nervous system. Often, our best guess is that such “messy” connections are close to random, while obeying certain statistical constraints, e.g. the number of connections per neuron. However, neuronal wiring is coordinated across larger mesoscopic distances in a way that differentiates between brain layers, areas, and groups of cells. We work across spatial scales in order to understand this hierarchy of order and disorder in brain networks. Ultimately, the goal is to understand how network structure is important for brain function. This leads to: 1. An inference technique which reconstructs mesoscopic brain networks from tracing experiments targeting spatially contiguous groups of neurons. 2. Models of networks which are random, while also having constrained average connectivity and group structure. 3. Comparing simulated and real respiratory rhythms, highlighting the role of inhibitory neurons and connectivity on rhythmogenesis, in particular synchrony and irregularity.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.rightsnone
dc.subjectBipartite
dc.subjectGraph theory
dc.subjectInference
dc.subjectNetworks
dc.subjectNeuroscience
dc.subjectRhythms
dc.subjectMathematics
dc.subjectNeurosciences
dc.subjectStatistics
dc.subject.otherApplied mathematics
dc.titleThis Brain Is a Mess: Inference, Random Graphs, and Biophysics to Disentangle Neuronal Networks
dc.typeThesis
dc.embargo.termsOpen Access


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