Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications
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, including LIF and NLIF, for constructing SNNs and investigates their potential ap...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fncom.2023.1215824/full |
| _version_ | 1797737589164212224 |
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| author | Sanaullah Shamini Koravuna Ulrich Rückert Thorsten Jungeblut |
| author_facet | Sanaullah Shamini Koravuna Ulrich Rückert Thorsten Jungeblut |
| author_sort | Sanaullah |
| collection | DOAJ |
| description | 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, including LIF and NLIF, 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. |
| first_indexed | 2024-03-12T13:30:32Z |
| format | Article |
| id | doaj.art-81cecaf3db87441cac3e9f04f4c7392d |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-03-12T13:30:32Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-81cecaf3db87441cac3e9f04f4c7392d2023-08-24T13:50:28ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882023-08-011710.3389/fncom.2023.12158241215824Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications Sanaullah0Shamini Koravuna1Ulrich Rückert2Thorsten Jungeblut3Industrial the Internet of Things, Department of Engineering and Mathematics, Bielefeld University of Applied Sciences and Arts, Bielefeld, GermanyAG Kognitronik & Sensorik, Technical Faculty, Universität Bielefeld, Bielefeld, GermanyAG Kognitronik & Sensorik, Technical Faculty, Universität Bielefeld, Bielefeld, GermanyIndustrial the Internet of Things, Department of Engineering and Mathematics, Bielefeld University of Applied Sciences and Arts, Bielefeld, GermanyThis 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, including LIF and NLIF, 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.https://www.frontiersin.org/articles/10.3389/fncom.2023.1215824/fullspiking neural networksneuron behaviorperformance comparisonclassification tasksbiological plausibilitycomputational model |
| spellingShingle | Sanaullah Shamini Koravuna Ulrich Rückert Thorsten Jungeblut Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications Frontiers in Computational Neuroscience spiking neural networks neuron behavior performance comparison classification tasks biological plausibility computational model |
| title | Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications |
| title_full | Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications |
| title_fullStr | Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications |
| title_full_unstemmed | Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications |
| title_short | Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications |
| title_sort | exploring spiking neural networks a comprehensive analysis of mathematical models and applications |
| topic | spiking neural networks neuron behavior performance comparison classification tasks biological plausibility computational model |
| url | https://www.frontiersin.org/articles/10.3389/fncom.2023.1215824/full |
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