Koziol, ScottBrown, NathanielBaylor University.2021-05-202021-05-2020212021-05-20https://hdl.handle.net/2104/11302Research has been done on various neural circuits, and the next step in many cases is creating a physical implementation. Programmable technologies such as the Field Programmable Analog Array (FPAA) combined with helper tools can allow circuit construction at high or low levels of detail, partly bridging the gap between simulation and implementation. This thesis combines previous research on silicon neurons with generating stochastic random numbers, targeting how it may be applied to stochastic resonance and implemented on an FPAA. Particular focus is given to converting a noise amplifier into a form with adjustable variance. Documenting the process of designing circuits for a hardware realization on the FPAA will provide guidance for similar work in the future and make it easier to build up to testing the theory of stochastic resonance.en-USBaylor University projects are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact libraryquestions@baylor.edu for inquiries about permission.Neuromorphic computing.Field Programmable Analog Array.FPAA.Silicon neurons.Stochastic resonance.ThesisWorldwide access.Access changed 9/21/23.