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dc.contributor.advisorKoziol, Scott M.
dc.creatorBoline, Jacob, 1992-
dc.date.accessioned2021-01-28T15:38:24Z
dc.date.available2021-01-28T15:38:24Z
dc.date.created2020-12
dc.date.issued2020-11-17
dc.date.submittedDecember 2020
dc.identifier.urihttps://hdl.handle.net/2104/11209
dc.description.abstractStochastic computing is an alternative method to standard deterministic methods. This thesis explores stochastic resonance in a spiking neuron, and leverages the effects it has to implement stochastic computing in an analog spiking neuron, and simple neuron network. In addition, a method for interfacing a Stochastic Artificial Neural Network implemented on a Field Programmable Gate Array with a computer running MATLAB is presented.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subjectStochastic computing. Stochastic resonance. Artificial neural network.
dc.titleStochastic computing & stochastic resonance in digital & analog neuromorphic systems.
dc.typeThesis
dc.rights.accessrightsNo access – contact librarywebmaster@baylor.edu
dc.type.materialtext
thesis.degree.nameM.S.E.C.E.
thesis.degree.departmentBaylor University. Dept. of Electrical & Computer Engineering.
thesis.degree.grantorBaylor University
thesis.degree.levelMasters
dc.date.updated2021-01-28T15:38:24Z
local.embargo.lift2025-12-01
local.embargo.terms2025-12-01


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