The Dynamics of Balanced Neural Networks under Spike-Timing Dependent Plasticity

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dc.contributor.advisorJosić, Krešimir
dc.contributor.committeeMemberTörök, Andrew
dc.contributor.committeeMemberOtt, William
dc.contributor.committeeMemberRosenbaum, Robert
dc.creatorAkil, Alan Eric
dc.creator.orcid0000-0003-4862-4771
dc.date.accessioned2021-09-01T19:21:12Z
dc.date.createdMay 2021
dc.date.issued2021-05
dc.date.submittedMay 2021
dc.date.updated2021-09-01T19:21:13Z
dc.description.abstractThe dynamics of local cortical networks are irregular, but correlated. Dynamic excitatory-inhibitory balance is a plausible mechanism that generates such irregular activity, but it remains unclear how balance is achieved and maintained in plastic neural networks. In particular, it is not fully understood how plasticity induced changes in the network affect balance, and in turn, how correlated, balanced activity impacts the structure of the network, and, ultimately, learning. How do the dynamics of balanced networks change under different plasticity rules? How does correlated spiking activity in recurrent networks change the evolution of weights, their eventual magnitude, and weight structure across the network? To address these questions, we develop a theory of spike-timing dependent plasticity in balanced networks. We show that balance can be attained and maintained under plasticity-induced weight changes. We find that correlations in the input mildly affect the evolution of synaptic weights. Under certain plasticity rules, we find an emergence of correlations between firing rates and synaptic weights. Under these rules, synaptic weights converge to a stable manifold in weight space with their final configuration dependent on the initial state of the network. Lastly, we show that our framework can describe the dynamics of plastic, balanced networks when subsets of neurons receive targeted optogenetic input.
dc.description.departmentMathematics, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10657/8131
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectComputational Neuroscience, Balanced Networks, Synaptic Plasticity, Spike-Timing Dependent Plasticity, Recurrent Neural Networks, Correlations
dc.titleThe Dynamics of Balanced Neural Networks under Spike-Timing Dependent Plasticity
dc.type.dcmiText
dc.type.genreThesis
local.embargo.lift2023-05-01
local.embargo.terms2023-05-01
thesis.degree.collegeCollege of Natural Sciences and Mathematics
thesis.degree.departmentMathematics, Department of
thesis.degree.disciplineMathematics
thesis.degree.grantorUniversity of Houston
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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