Spiking Neural Network Applications
| Autor: | Celik, Gaffari, Talu, M. Fatih |
|---|---|
| Přispěvatelé: | Belirlenecek |
| Jazyk: | turečtina |
| Rok vydání: | 2017 |
| Předmět: | |
| Popis: | 2017 International Artificial Intelligence and Data Processing Symposium (IDAP) -- SEP 16-17, 2017 -- Malatya, TURKEY 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. IEEE Turkey Sect, Anatolian Sci |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|