A Review of Recent Advances and Application for Spiking Neural Networks
Spiking neural network (SNN) is a new generation of artificial neural network. It is more biologically plausible and has been widely concerned by scholars owing to its unique information coding schemes, rich spatiotemporal dynamics, and event-driven operating mode with low power. In recent years, SN...
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| Format: | Article |
| Language: | zho |
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《中国工程科学》杂志社
2023-12-01
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| Series: | 中国工程科学 |
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| Online Access: | http://www.engineering.org.cn/en/10.15302/J-SSCAE-2023.06.011 |
| _version_ | 1797363793557192704 |
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| author | Liu Hao Chai Hongfeng |
| author_facet | Liu Hao Chai Hongfeng |
| author_sort | Liu Hao |
| collection | DOAJ |
| description | Spiking neural network (SNN) is a new generation of artificial neural network. It is more biologically plausible and has been widely concerned by scholars owing to its unique information coding schemes, rich spatiotemporal dynamics, and event-driven operating mode with low power. In recent years, SNN has been explored and applied in many fields such as medical health, industrial detection, and intelligent driving. First, the basic elements and learning algorithms of SNN are introduced, including classical spiking neuron models, spike-timing dependent plasticity (STDP), and common information coding methods. The advantages and disadvantages of the learning algorithms are also analyzed. Then, the mainstream software simulators and neuromorphic hardware of SNN are summarized. Subsequently, the research progress and application scenarios of SNN in terms of computer vision, natural language processing, and reasoning decision are introduced. Particularly, SNN has shown strong potentials in tasks such as object detection, action recognition, semantic cognition, and speech recognition, significantly improving computational performance. Future research and application of SNN should focus on strengthening the research on key core technologies, promoting the application of technological achievements, and continuously optimizing the industrial ecology, thus to catch up with the advanced international level. Moreover, continuous research and breakthroughs of brain-inspired systems and control theories will promote the establishment of large-scale SNN models and are expected to broaden the application prospect of artificial intelligence.
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| first_indexed | 2024-03-08T16:25:11Z |
| format | Article |
| id | doaj.art-2cee47836c544fae80de33273076f7a6 |
| institution | Directory Open Access Journal |
| issn | 2096-0034 |
| language | zho |
| last_indexed | 2024-03-08T16:25:11Z |
| publishDate | 2023-12-01 |
| publisher | 《中国工程科学》杂志社 |
| record_format | Article |
| series | 中国工程科学 |
| spelling | doaj.art-2cee47836c544fae80de33273076f7a62024-01-07T07:37:49Zzho《中国工程科学》杂志社中国工程科学2096-00342023-12-01256617910.15302/J-SSCAE-2023.06.011A Review of Recent Advances and Application for Spiking Neural NetworksLiu Hao 0 Chai Hongfeng 1 Fundamental Technology Center, China Construction Bank Financial Technology Co., Ltd., Shanghai 200120, China School of Computer Science, Fudan University, Shanghai 200438, China ; Institute of Financial Technology, Fudan University, Shanghai 200438, China Spiking neural network (SNN) is a new generation of artificial neural network. It is more biologically plausible and has been widely concerned by scholars owing to its unique information coding schemes, rich spatiotemporal dynamics, and event-driven operating mode with low power. In recent years, SNN has been explored and applied in many fields such as medical health, industrial detection, and intelligent driving. First, the basic elements and learning algorithms of SNN are introduced, including classical spiking neuron models, spike-timing dependent plasticity (STDP), and common information coding methods. The advantages and disadvantages of the learning algorithms are also analyzed. Then, the mainstream software simulators and neuromorphic hardware of SNN are summarized. Subsequently, the research progress and application scenarios of SNN in terms of computer vision, natural language processing, and reasoning decision are introduced. Particularly, SNN has shown strong potentials in tasks such as object detection, action recognition, semantic cognition, and speech recognition, significantly improving computational performance. Future research and application of SNN should focus on strengthening the research on key core technologies, promoting the application of technological achievements, and continuously optimizing the industrial ecology, thus to catch up with the advanced international level. Moreover, continuous research and breakthroughs of brain-inspired systems and control theories will promote the establishment of large-scale SNN models and are expected to broaden the application prospect of artificial intelligence. http://www.engineering.org.cn/en/10.15302/J-SSCAE-2023.06.011spiking neural network brain-inspired computing learning algorithm neuromorphic chip application scenario |
| spellingShingle | Liu Hao Chai Hongfeng A Review of Recent Advances and Application for Spiking Neural Networks 中国工程科学 spiking neural network brain-inspired computing learning algorithm neuromorphic chip application scenario |
| title | A Review of Recent Advances and Application for Spiking Neural Networks |
| title_full | A Review of Recent Advances and Application for Spiking Neural Networks |
| title_fullStr | A Review of Recent Advances and Application for Spiking Neural Networks |
| title_full_unstemmed | A Review of Recent Advances and Application for Spiking Neural Networks |
| title_short | A Review of Recent Advances and Application for Spiking Neural Networks |
| title_sort | review of recent advances and application for spiking neural networks |
| topic | spiking neural network brain-inspired computing learning algorithm neuromorphic chip application scenario |
| url | http://www.engineering.org.cn/en/10.15302/J-SSCAE-2023.06.011 |
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