Stochastic computing and stochastic resonance demonstrated in custom analog neuromorphic hardware.
| dc.contributor.advisor | Koziol, Scott M. | |
| dc.creator | Coker, Cameron, 1997- | |
| dc.creator.orcid | 0000-0001-9142-4279 | |
| dc.date.accessioned | 2022-06-03T13:13:28Z | |
| dc.date.available | 2022-06-03T13:13:28Z | |
| dc.date.created | 2022-05 | |
| dc.date.issued | 2022-01-21 | |
| dc.date.submitted | May 2022 | |
| dc.date.updated | 2022-06-03T13:13:29Z | |
| dc.description.abstract | Stochastic computing offers an alternative computing method to standard systems. Stochastic resonance is a means of leveraging noise to improve system performance. This thesis applies both concepts to spiking analog neurons. The general usefulness of stochastic resonance is tested while the principles of stochastic resonance are applied to determine the viability of a stochastic spiking neural network. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | https://hdl.handle.net/2104/11903 | |
| dc.language.iso | en | |
| dc.rights.accessrights | No access – contact librarywebmaster@baylor.edu | |
| dc.subject | Stochastic computing. Stochastic resonance. Neuromorphic. | |
| dc.title | Stochastic computing and stochastic resonance demonstrated in custom analog neuromorphic hardware. | |
| dc.type | Thesis | |
| dc.type.material | text | |
| local.embargo.lift | 2027-05-01 | |
| local.embargo.terms | 2027-05-01 | |
| thesis.degree.department | Baylor University. Dept. of Electrical & Computer Engineering. | |
| thesis.degree.grantor | Baylor University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | M.S.E.C.E. |
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