Spiking neural network tactile classification method with faster and more accurate membrane potential representation
Abstract Robot perception is an important topic in artificial intelligence field, and tactile recognition in particular is indispensable for human–computer interaction. Efficiently classifying data obtained by touch sensors has long been an issue. In recent years, spiking neural networks (SNNs) have...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Collaborative Intelligent Manufacturing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cim2.70004 |
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| _version_ | 1846102072083087360 |
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| author | Jing Yang Zukun Yu Xiaoyang Ji Zhidong Su Shaobo Li Yang Cao |
| author_facet | Jing Yang Zukun Yu Xiaoyang Ji Zhidong Su Shaobo Li Yang Cao |
| author_sort | Jing Yang |
| collection | DOAJ |
| description | Abstract Robot perception is an important topic in artificial intelligence field, and tactile recognition in particular is indispensable for human–computer interaction. Efficiently classifying data obtained by touch sensors has long been an issue. In recent years, spiking neural networks (SNNs) have been widely used in tactile data categorisation due to their temporal information processing benefits, low power consumption, and high biological dependability. However, traditional SNN classification methods often encounter under‐convergence when using membrane potential representation, decreasing their classification accuracy. Meanwhile, due to the time‐discrete nature of SNN models, classification requires a significant time overhead, which restricts their real‐time tactile sensing application potential. Considering these concerns, the authors propose a faster and more accurate SNN tactile classification approach using improved membrane potential representation. This method effectively overcomes model convergence problems by optimising the membrane potential expression and the relationship between the loss function and network parameters while significantly reducing the time overhead and enhancing the classification accuracy and robustness of the model. The experimental results show that the propose approach improves the classification accuracy by 4.16% and 2.71% and reduces the overall time by 8.00% and 8.14% on the EvTouch‐Containers dataset and EvTouch‐Objects dataset, respectively, when compared with existing models. |
| format | Article |
| id | doaj-art-88c28069fc4c4b56aca63d6fa40a8b7f |
| institution | Kabale University |
| issn | 2516-8398 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Collaborative Intelligent Manufacturing |
| spelling | doaj-art-88c28069fc4c4b56aca63d6fa40a8b7f2024-12-28T04:20:30ZengWileyIET Collaborative Intelligent Manufacturing2516-83982024-12-0164n/an/a10.1049/cim2.70004Spiking neural network tactile classification method with faster and more accurate membrane potential representationJing Yang0Zukun Yu1Xiaoyang Ji2Zhidong Su3Shaobo Li4Yang Cao5Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education Guizhou University Guiyang ChinaKey Laboratory of Advanced Manufacturing Technology of the Ministry of Education Guizhou University Guiyang ChinaState Key Laboratory of Public Big Data Ministry of Education Guizhou University Guiyang ChinaSchool of Electrical and Computer Engineering Oklahoma State University Stillwater Oklahoma USAKey Laboratory of Advanced Manufacturing Technology of the Ministry of Education Guizhou University Guiyang ChinaSchool of Mechanical Engineering Guizhou University Guiyang ChinaAbstract Robot perception is an important topic in artificial intelligence field, and tactile recognition in particular is indispensable for human–computer interaction. Efficiently classifying data obtained by touch sensors has long been an issue. In recent years, spiking neural networks (SNNs) have been widely used in tactile data categorisation due to their temporal information processing benefits, low power consumption, and high biological dependability. However, traditional SNN classification methods often encounter under‐convergence when using membrane potential representation, decreasing their classification accuracy. Meanwhile, due to the time‐discrete nature of SNN models, classification requires a significant time overhead, which restricts their real‐time tactile sensing application potential. Considering these concerns, the authors propose a faster and more accurate SNN tactile classification approach using improved membrane potential representation. This method effectively overcomes model convergence problems by optimising the membrane potential expression and the relationship between the loss function and network parameters while significantly reducing the time overhead and enhancing the classification accuracy and robustness of the model. The experimental results show that the propose approach improves the classification accuracy by 4.16% and 2.71% and reduces the overall time by 8.00% and 8.14% on the EvTouch‐Containers dataset and EvTouch‐Objects dataset, respectively, when compared with existing models.https://doi.org/10.1049/cim2.70004data analysishuman‐robot interactionneural nets |
| spellingShingle | Jing Yang Zukun Yu Xiaoyang Ji Zhidong Su Shaobo Li Yang Cao Spiking neural network tactile classification method with faster and more accurate membrane potential representation IET Collaborative Intelligent Manufacturing data analysis human‐robot interaction neural nets |
| title | Spiking neural network tactile classification method with faster and more accurate membrane potential representation |
| title_full | Spiking neural network tactile classification method with faster and more accurate membrane potential representation |
| title_fullStr | Spiking neural network tactile classification method with faster and more accurate membrane potential representation |
| title_full_unstemmed | Spiking neural network tactile classification method with faster and more accurate membrane potential representation |
| title_short | Spiking neural network tactile classification method with faster and more accurate membrane potential representation |
| title_sort | spiking neural network tactile classification method with faster and more accurate membrane potential representation |
| topic | data analysis human‐robot interaction neural nets |
| url | https://doi.org/10.1049/cim2.70004 |
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