Evaluating Spiking Neural Network Models: A Comparative Performance Analysis
Ullah S, Koravuna S, Rückert U, Jungeblut T (2023) .
Kurzbeitrag Konferenz / Poster | Englisch
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Einrichtung
Abstract / Bemerkung
The challenge of determining the most suitable model was addressed by comparing the performance of different models, and the results were analyzed to determine the most effective one. Overall, this study sheds light on the challenges and potential benefits of SNNs and their models. It highlights the importance of comparing different models to determine the most suitable and provides valuable insights for researchers and practitioners working in this area.
Erscheinungsjahr
2023
Page URI
https://pub.uni-bielefeld.de/record/2982810
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Ullah S, Koravuna S, Rückert U, Jungeblut T. Evaluating Spiking Neural Network Models: A Comparative Performance Analysis.
Ullah, S., Koravuna, S., Rückert, U., & Jungeblut, T. (2023). Evaluating Spiking Neural Network Models: A Comparative Performance Analysis. Presented at the . https://doi.org/10.13140/RG.2.2.21295.71847
Ullah, Sana, Koravuna, Shamini, Rückert, Ulrich, and Jungeblut, Thorsten. 2023. “Evaluating Spiking Neural Network Models: A Comparative Performance Analysis”. Presented at the . Bielefeld : Datatninja Spring School 2023.
Ullah, S., Koravuna, S., Rückert, U., and Jungeblut, T. (2023).“Evaluating Spiking Neural Network Models: A Comparative Performance Analysis”.
Ullah, S., et al., 2023. Evaluating Spiking Neural Network Models: A Comparative Performance Analysis.
S. Ullah, et al., “Evaluating Spiking Neural Network Models: A Comparative Performance Analysis”, Bielefeld : Datatninja Spring School 2023, 2023.
Ullah, S., Koravuna, S., Rückert, U., Jungeblut, T.: Evaluating Spiking Neural Network Models: A Comparative Performance Analysis. (2023).
Ullah, Sana, Koravuna, Shamini, Rückert, Ulrich, and Jungeblut, Thorsten. “Evaluating Spiking Neural Network Models: A Comparative Performance Analysis”., Bielefeld : Datatninja Spring School 2023, 2023.
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