Understanding the functional roles of modelling components in spiking neural networks
Spiking neural networks (SNNs), inspired by the neural circuits of the brain, are promising in achieving high computational efficiency with biological fidelity. Nevertheless, it is quite difficult to optimize SNNs because the functional roles of their modelling components remain unclear. By designin...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2024-01-01
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| Series: | Neuromorphic Computing and Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2634-4386/ad6cef |
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| _version_ | 1846149632043778048 |
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| author | Huifeng Yin Hanle Zheng Jiayi Mao Siyuan Ding Xing Liu Mingkun Xu Yifan Hu Jing Pei Lei Deng |
| author_facet | Huifeng Yin Hanle Zheng Jiayi Mao Siyuan Ding Xing Liu Mingkun Xu Yifan Hu Jing Pei Lei Deng |
| author_sort | Huifeng Yin |
| collection | DOAJ |
| description | Spiking neural networks (SNNs), inspired by the neural circuits of the brain, are promising in achieving high computational efficiency with biological fidelity. Nevertheless, it is quite difficult to optimize SNNs because the functional roles of their modelling components remain unclear. By designing and evaluating several variants of the classic model, we systematically investigate the functional roles of key modelling components, leakage, reset, and recurrence, in leaky integrate-and-fire (LIF) based SNNs. Through extensive experiments, we demonstrate how these components influence the accuracy, generalization, and robustness of SNNs. Specifically, we find that the leakage plays a crucial role in balancing memory retention and robustness, the reset mechanism is essential for uninterrupted temporal processing and computational efficiency, and the recurrence enriches the capability to model complex dynamics at a cost of robustness degradation. With these interesting observations, we provide optimization suggestions for enhancing the performance of SNNs in different scenarios. This work deepens the understanding of how SNNs work, which offers valuable guidance for the development of more effective and robust neuromorphic models. |
| format | Article |
| id | doaj-art-eedc1f0716694835b37bb95be39befa8 |
| institution | Kabale University |
| issn | 2634-4386 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Neuromorphic Computing and Engineering |
| spelling | doaj-art-eedc1f0716694835b37bb95be39befa82024-11-29T14:19:33ZengIOP PublishingNeuromorphic Computing and Engineering2634-43862024-01-014303400910.1088/2634-4386/ad6cefUnderstanding the functional roles of modelling components in spiking neural networksHuifeng Yin0Hanle Zheng1Jiayi Mao2Siyuan Ding3https://orcid.org/0009-0004-4722-9053Xing Liu4Mingkun Xu5Yifan Hu6Jing Pei7Lei Deng8https://orcid.org/0000-0002-5172-9411Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University , Beijing, People’s Republic of ChinaCenter for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University , Beijing, People’s Republic of ChinaCenter for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University , Beijing, People’s Republic of ChinaWeiyang College, Tsinghua University , Beijing, People’s Republic of ChinaCollege of Electronic Information and Automation, Tianjin University of Science and Technology , Tianjin, People’s Republic of ChinaGuangdong Institute of Intelligence Science and Technology , Zhuhai, People’s Republic of ChinaCenter for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University , Beijing, People’s Republic of ChinaCenter for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University , Beijing, People’s Republic of ChinaCenter for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University , Beijing, People’s Republic of ChinaSpiking neural networks (SNNs), inspired by the neural circuits of the brain, are promising in achieving high computational efficiency with biological fidelity. Nevertheless, it is quite difficult to optimize SNNs because the functional roles of their modelling components remain unclear. By designing and evaluating several variants of the classic model, we systematically investigate the functional roles of key modelling components, leakage, reset, and recurrence, in leaky integrate-and-fire (LIF) based SNNs. Through extensive experiments, we demonstrate how these components influence the accuracy, generalization, and robustness of SNNs. Specifically, we find that the leakage plays a crucial role in balancing memory retention and robustness, the reset mechanism is essential for uninterrupted temporal processing and computational efficiency, and the recurrence enriches the capability to model complex dynamics at a cost of robustness degradation. With these interesting observations, we provide optimization suggestions for enhancing the performance of SNNs in different scenarios. This work deepens the understanding of how SNNs work, which offers valuable guidance for the development of more effective and robust neuromorphic models.https://doi.org/10.1088/2634-4386/ad6cefspiking neural networksneuromorphic computingfunctional rolesmodelling componentsrobustness |
| spellingShingle | Huifeng Yin Hanle Zheng Jiayi Mao Siyuan Ding Xing Liu Mingkun Xu Yifan Hu Jing Pei Lei Deng Understanding the functional roles of modelling components in spiking neural networks Neuromorphic Computing and Engineering spiking neural networks neuromorphic computing functional roles modelling components robustness |
| title | Understanding the functional roles of modelling components in spiking neural networks |
| title_full | Understanding the functional roles of modelling components in spiking neural networks |
| title_fullStr | Understanding the functional roles of modelling components in spiking neural networks |
| title_full_unstemmed | Understanding the functional roles of modelling components in spiking neural networks |
| title_short | Understanding the functional roles of modelling components in spiking neural networks |
| title_sort | understanding the functional roles of modelling components in spiking neural networks |
| topic | spiking neural networks neuromorphic computing functional roles modelling components robustness |
| url | https://doi.org/10.1088/2634-4386/ad6cef |
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