Spiking Neural Network Applications
| dc.authorid | Talu, Muhammed Fatih/0000-0003-1166-8404 | |
| dc.authorwosid | Talu, Muhammed Fatih/W-2834-2017 | |
| dc.contributor.author | Celik, Gaffari | |
| dc.contributor.author | Talu, M. Fatih | |
| dc.date.accessioned | 2024-08-04T20:44:10Z | |
| dc.date.available | 2024-08-04T20:44:10Z | |
| dc.date.issued | 2017 | |
| dc.department | İnönü Üniversitesi | en_US |
| dc.description | 2017 International Artificial Intelligence and Data Processing Symposium (IDAP) -- SEP 16-17, 2017 -- Malatya, TURKEY | en_US |
| dc.description.abstract | Spiking Neural Network (SNN) are 3rd Generation Artificial Neural Networks (ANN) models. The fact that time information is processed in the form of spikes and there are multiple synapses between cells (neurons) are the most important features that distinguish SNN from previous generations. In this study, artificial learning systems which can learn by using basic logical operators such as AND, OR, XOR have been developed in order to understand SNN structure. In SNN, we tried to find optimal values for these parameters by examining the effect of the number of connections between cells and delays between connections to learning success. | en_US |
| dc.description.sponsorship | IEEE Turkey Sect,Anatolian Sci | en_US |
| dc.identifier.isbn | 978-1-5386-1880-6 | |
| dc.identifier.scopus | 2-s2.0-85039901955 | en_US |
| dc.identifier.scopusquality | N/A | en_US |
| dc.identifier.uri | https://hdl.handle.net/11616/98080 | |
| dc.identifier.wos | WOS:000426868700106 | en_US |
| dc.identifier.wosquality | N/A | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | tr | en_US |
| dc.publisher | Ieee | en_US |
| dc.relation.ispartof | 2017 International Artificial Intelligence and Data Processing Symposium (Idap) | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Artificial Neural Networks | en_US |
| dc.subject | Spiking Neural Network | en_US |
| dc.subject | Delay Time | en_US |
| dc.subject | Synapses | en_US |
| dc.subject | BackPropagation | en_US |
| dc.subject | Population Coding | en_US |
| dc.subject | Gaussian Receptive Fields | en_US |
| dc.title | Spiking Neural Network Applications | en_US |
| dc.type | Conference Object | en_US |
