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 |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 2516-8398 |