Damage Resistance of an fMRI-Spiking Neural Network Based on Speech Recognition Against Stochastic Attack
Brain-like models are commonly used for pattern recognition, but they face significant performance degradation in neuromorphic hardware when exposed to complex electromagnetic environments. The human brain has adaptability to the exterior attack, and we expect that incorporating bio-plausibility int...
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MDPI AG
2025-06-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/10/7/415 |
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| author | Lei Guo Huan Liu Yihua Song Nancheng Ma |
| author_facet | Lei Guo Huan Liu Yihua Song Nancheng Ma |
| author_sort | Lei Guo |
| collection | DOAJ |
| description | Brain-like models are commonly used for pattern recognition, but they face significant performance degradation in neuromorphic hardware when exposed to complex electromagnetic environments. The human brain has adaptability to the exterior attack, and we expect that incorporating bio-plausibility into a brain-like model will enhance its robustness. However, brain-like models currently lack bio-plausibility. Therefore, we construct a spiking neural network (SNN) whose topology is constrained by human brain functional Magnetic Resonance Imaging (fMRI), called fMRI-SNN. To certify its damage resistance, we investigate speech recognition accuracy against stochastic attack. To reveal its damage-resistant mechanism, we explore the neural electrical features, adaptive modulation of synaptic plasticity, and topological features against stochastic attack. Research shows that fMRI-SNN surpasses SNNs with distinct topologies in recognition accuracy against stochastic attack, notably maintaining similar accuracy levels before and after stochastic attacks when the damage proportion is below 30%, demonstrating that our method improves the damage resistance of brain-like models. In addition, the change in neural electrical activity serves as interior manifestation, corresponding to the damage resistance of SNNs for recognition tasks, while the synaptic plasticity serves as the inherent determinant of the damage resistance, and the topology serves as a determinant impacting the damage resistance. |
| format | Article |
| id | doaj-art-74061bfcfba24bab832b7cc3411bd3fa |
| institution | Kabale University |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-74061bfcfba24bab832b7cc3411bd3fa2025-08-20T03:58:30ZengMDPI AGBiomimetics2313-76732025-06-0110741510.3390/biomimetics10070415Damage Resistance of an fMRI-Spiking Neural Network Based on Speech Recognition Against Stochastic AttackLei Guo0Huan Liu1Yihua Song2Nancheng Ma3Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300131, ChinaTianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300131, ChinaTianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300131, ChinaTianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300131, ChinaBrain-like models are commonly used for pattern recognition, but they face significant performance degradation in neuromorphic hardware when exposed to complex electromagnetic environments. The human brain has adaptability to the exterior attack, and we expect that incorporating bio-plausibility into a brain-like model will enhance its robustness. However, brain-like models currently lack bio-plausibility. Therefore, we construct a spiking neural network (SNN) whose topology is constrained by human brain functional Magnetic Resonance Imaging (fMRI), called fMRI-SNN. To certify its damage resistance, we investigate speech recognition accuracy against stochastic attack. To reveal its damage-resistant mechanism, we explore the neural electrical features, adaptive modulation of synaptic plasticity, and topological features against stochastic attack. Research shows that fMRI-SNN surpasses SNNs with distinct topologies in recognition accuracy against stochastic attack, notably maintaining similar accuracy levels before and after stochastic attacks when the damage proportion is below 30%, demonstrating that our method improves the damage resistance of brain-like models. In addition, the change in neural electrical activity serves as interior manifestation, corresponding to the damage resistance of SNNs for recognition tasks, while the synaptic plasticity serves as the inherent determinant of the damage resistance, and the topology serves as a determinant impacting the damage resistance.https://www.mdpi.com/2313-7673/10/7/415spiking neural networkfunctional magnetic resonance imagingdamage resistancespeech recognition |
| spellingShingle | Lei Guo Huan Liu Yihua Song Nancheng Ma Damage Resistance of an fMRI-Spiking Neural Network Based on Speech Recognition Against Stochastic Attack Biomimetics spiking neural network functional magnetic resonance imaging damage resistance speech recognition |
| title | Damage Resistance of an fMRI-Spiking Neural Network Based on Speech Recognition Against Stochastic Attack |
| title_full | Damage Resistance of an fMRI-Spiking Neural Network Based on Speech Recognition Against Stochastic Attack |
| title_fullStr | Damage Resistance of an fMRI-Spiking Neural Network Based on Speech Recognition Against Stochastic Attack |
| title_full_unstemmed | Damage Resistance of an fMRI-Spiking Neural Network Based on Speech Recognition Against Stochastic Attack |
| title_short | Damage Resistance of an fMRI-Spiking Neural Network Based on Speech Recognition Against Stochastic Attack |
| title_sort | damage resistance of an fmri spiking neural network based on speech recognition against stochastic attack |
| topic | spiking neural network functional magnetic resonance imaging damage resistance speech recognition |
| url | https://www.mdpi.com/2313-7673/10/7/415 |
| work_keys_str_mv | AT leiguo damageresistanceofanfmrispikingneuralnetworkbasedonspeechrecognitionagainststochasticattack AT huanliu damageresistanceofanfmrispikingneuralnetworkbasedonspeechrecognitionagainststochasticattack AT yihuasong damageresistanceofanfmrispikingneuralnetworkbasedonspeechrecognitionagainststochasticattack AT nanchengma damageresistanceofanfmrispikingneuralnetworkbasedonspeechrecognitionagainststochasticattack |