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《中国工程科学》 >> 2023年 第25卷 第6期 doi: 10.15302/J-SSCAE-2023.06.011

脉冲神经网络研究现状与应用进展

1. 建信金融科技有限责任公司基础技术中心,上海 200120
2. 复旦大学计算机科学技术学院,上海 200438
3. 复旦大学 金融科技研究院,上海 200438
4. 中国银联股份有限公司金融科技研究院,上海 201201
5. 上海大学悉尼工商学院, 上海 200444

资助项目 :国家重点研发计划项目(2021YFC3300600);中国工程院咨询项目“数字化转型背景下金融风险监测与预警体系战略研究”(2023-XY-43);国家自然科学基金项目(72201161);长三角科技创新共同体联合攻关项目(2022CSJGG0800, 2021-YF09-00114-GX, PO3522083587, PO3522083675, HP2300490) 收稿日期: 2023-09-18 修回日期: 2023-11-08 发布日期: 2023-11-29

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摘要

脉冲神经网络(SNN)是更具生物可解释性的新一代人工神经网络,具有独特的信息编码处理方式、丰富的时空动力学特性、低功耗事件驱动工作模式等优势,近年来受到广泛关注并在医疗健康、工业检测、智能驾驶等方向获得探索性应用。本文介绍了SNN的基本要素和学习算法,包括经典的神经元模型、突触可塑性机制、常用的信息编码方式,分析了各 类学习算法的优缺点,总结了主流的SNN软件模拟器、脉冲神经形态硬件的研究情况;细致梳理了SNN在计算机视觉、自然语言处理、推理决策等方面的研究以及行业应用场景,发现SNN在目标检测、动作识别、语义认知、语音识别等任务中具有突出的潜力,显著提升了相应的计算性能。我国在SNN领域的研究与应用发展,重在加强关键核心技术攻关、推动技 术成果转化应用、持续优化产业生态格局,以尽快实现与国际先进水平的接轨;类脑复杂系统、类脑控制等理论与方法的深入研究和逐步突破,也将促进大规模SNN新模型的构建,有望拓展人工智能的更广阔应用前景。

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