脉冲神经网络的发展
PDF下载 (162)TALANOV Max,FEDOROVA Alina,KIPELKIN Ivan,VALLVERDU Jordi,EROKHIN Victor.脉冲神经网络的发展[J].宁波大学学报(理工版),2025,38(2):59-70.DOI:10.20098/j.cnki.1001-5132.2024.1213
TALANOVMax,FEDOROVAAlina,KIPELKIN Ivan,VALLVERDU Jordi,EROKHIN Victor.Evolution of spiking neural networks[J].Journal of Ningbo University(Natural Science & Engineering Edition),2025,38(2):59-70.DOI:10.20098/j.cnki.1001-5132.2024.1213
| Title: | Evolution of spiking neural networks |
| 作者: | TALANOV Max, FEDOROVA Alina, KIPELKIN Ivan, VALLVERDU Jordi, EROKHIN Victor |
| Author(s): | TALANOVMax, FEDOROVAAlina, KIPELKIN Ivan, VALLVERDU Jordi, EROKHIN Victor |
| 关键词: | 脉冲神经网络; 忆阻器; 相图; 高能效人工智能; 神经形态计算 |
| Keywords: | spiking neural networks; memristor; phase portraits; energy-efficient AI; neuromorphic computing |
| 分类号: | TP18 |
| DOI: | 10.20098/j.cnki.1001-5132.2024.1213 |
| 文献标识码: | A |
| 摘要: | 脉冲神经网络(SNNs)代表了一种生物启发计算框架, 它将神经科学和人工智能联系在一起, 在时序数据处理、能效和实时决策方面具有独特优势. 探讨了SNN技术的演化过程, 强调了其与高级学习机制的集成, 如尖峰时间依赖可塑性(STDP)和与深度学习架构的融合. 利用忆阻器作为纳米级突触设备, 显著提高了能效、适应性和可扩展性, 解决了神经形态计算中的关键问题. 通过相图和非线性动力学分析, 验证了系统在不同工作负载下的稳定性和鲁棒性. 这些进步使SNNs成为机器人、物联网和自适应低功耗人工智能系统等应用领域的一项变革性技术, 为神经形态硬件和混合学习范式的未来创新铺平了道路. |
| Abstract: | Spiking neural networks (SNNs) represent a biologically-inspired computational framework that bridges neuroscience and artificial intelligence, offering unique advantages in temporal data processing, energy efficiency, and real-time decision-making. This paper explores the evolution of SNN technologies, emphasizing their integration with advanced learning mechanisms such as spike-timing-dependent plasticity (STDP) and hybridization with deep learning architectures. Leveraging memristors as nanoscale synaptic devices, we demonstrate significant enhancements in energy efficiency, adaptability, and scalability, addressing key challenges in neuromorphic computing. Through phase portraits and nonlinear dynamics analysis, we validate the system’s stability and robustness under diverse workloads. These advancements position SNNs as a transformative technology for applications in robotics, IoT, and adaptive low-power AI systems, paving the way for future innovations in neuromorphic hardware and hybrid learning paradigms. |
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| 备注/Memo: | Received date: 2024−12−25. JOURNAL OF NINGBO UNIVERSITY ( NSEE ): http://journallg.nbu.edu.cn/ Foundation items: Supported by CUP (J53C22003010006, J43C24000230007); ICREA2019. Biography: TALANOV Max, doctor, specializing in: neuroscience and artificial intelligence. Email: max.talanov@gmail.com 宁波大学学报(理工版)网址:http://journallg.nbu.edu.cn/ |