Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications

Ullah S, Koravuna S, Rückert U, Jungeblut T (2023)
Frontiers in Computational Neuroscience 17.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Abstract / Bemerkung
This article presents a comprehensive analysis of spiking neural networks (SNNs) and their mathematical models for simulating the behavior of neurons through the generation of spikes. The study explores various models, includingLIFandNLIF, for constructing SNNs and investigates their potential applications in different domains. However, implementation poses several challenges, including identifying the most appropriate model for classification tasks that demand high accuracy and low-performance loss. To address this issue, this research study compares the performance, behavior, and spike generation of multiple SNN models using consistent inputs and neurons. The findings of the study provide valuable insights into the benefits and challenges of SNNs and their models, emphasizing the significance of comparing multiple models to identify the most effective one. Moreover, the study quantifies the number of spiking operations required by each model to process the same inputs and produce equivalent outputs, enabling a thorough assessment of computational efficiency. The findings provide valuable insights into the benefits and limitations of SNNs and their models. The research underscores the significance of comparing different models to make informed decisions in practical applications. Additionally, the results reveal essential variations in biological plausibility and computational efficiency among the models, further emphasizing the importance of selecting the most suitable model for a given task. Overall, this study contributes to a deeper understanding of SNNs and offers practical guidelines for using their potential in real-world scenarios.
Erscheinungsjahr
2023
Zeitschriftentitel
Frontiers in Computational Neuroscience
Band
17
eISSN
1662-5188
Page URI
https://pub.uni-bielefeld.de/record/2982807

Zitieren

Ullah S, Koravuna S, Rückert U, Jungeblut T. Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications. Frontiers in Computational Neuroscience. 2023;17.
Ullah, S., Koravuna, S., Rückert, U., & Jungeblut, T. (2023). Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications. Frontiers in Computational Neuroscience, 17. https://doi.org/10.3389/fncom.2023.1215824
Ullah, Sana, Koravuna, Shamini, Rückert, Ulrich, and Jungeblut, Thorsten. 2023. “Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications”. Frontiers in Computational Neuroscience 17.
Ullah, S., Koravuna, S., Rückert, U., and Jungeblut, T. (2023). Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications. Frontiers in Computational Neuroscience 17.
Ullah, S., et al., 2023. Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications. Frontiers in Computational Neuroscience, 17.
S. Ullah, et al., “Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications”, Frontiers in Computational Neuroscience, vol. 17, 2023.
Ullah, S., Koravuna, S., Rückert, U., Jungeblut, T.: Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications. Frontiers in Computational Neuroscience. 17, (2023).
Ullah, Sana, Koravuna, Shamini, Rückert, Ulrich, and Jungeblut, Thorsten. “Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications”. Frontiers in Computational Neuroscience 17 (2023).
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2024-02-08T14:01:59Z
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