Research on Anti-Interference Performance of Spiking Neural Network Under Network Connection Damage
Therefore, under the same network connection damage conditions, spiking neural networks exhibit unique anti-interference performance advantages. The performance of spiking neural networks in information processing and pattern recognition is relatively more stable and outstanding. Further analysis reveals that factors such as network structure, encoding method, and learning algorithm have a significant impact on the anti-interference performance of both.
Authors
- Guilei Ma (ORCID: https://orcid.org/0000-0001-9778-7177)
- Haijie Pang (ORCID: https://orcid.org/0009-0005-9149-023X)
- Menghua Man (ORCID: https://orcid.org/0000-0002-6816-3709)
- Yongqiang Zhang (ORCID: https://orcid.org/0000-0001-5723-0446)
- Jinlong Ma (ORCID: https://orcid.org/0000-0002-4288-6327)
- Xiaoming Zhang (ORCID: https://orcid.org/0000-0002-3134-9294)
Institutions
- Hebei University of Science and Technology (CN)
- PLA Army Engineering University (CN)
- Hangzhou Dianzi University (CN)
Publication Details
- Journal
- Brain Sciences
- Published
- 2025-02-20
- DOI
- https://doi.org/10.3390/brainsci15030217
- Citations
- 1
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.73
Funders
- Natural Science Foundation of Hebei Province