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适合类脑脉冲神经网络的应用任务范式分析与展望

张铁林 李澄宇 王刚 张马路 余磊 徐波

张铁林, 李澄宇, 王刚, 张马路, 余磊, 徐波. 适合类脑脉冲神经网络的应用任务范式分析与展望[J]. 电子与信息学报, 2023, 45(8): 2675-2688. doi: 10.11999/JEIT221459
引用本文: 张铁林, 李澄宇, 王刚, 张马路, 余磊, 徐波. 适合类脑脉冲神经网络的应用任务范式分析与展望[J]. 电子与信息学报, 2023, 45(8): 2675-2688. doi: 10.11999/JEIT221459
ZHANG Tielin, LI Chengyu, WANG Gang, ZHANG Malu, YU Lei, XU Bo. Research Advances and New Paradigms for Biology-inspired Spiking Neural Networks[J]. Journal of Electronics & Information Technology, 2023, 45(8): 2675-2688. doi: 10.11999/JEIT221459
Citation: ZHANG Tielin, LI Chengyu, WANG Gang, ZHANG Malu, YU Lei, XU Bo. Research Advances and New Paradigms for Biology-inspired Spiking Neural Networks[J]. Journal of Electronics & Information Technology, 2023, 45(8): 2675-2688. doi: 10.11999/JEIT221459

适合类脑脉冲神经网络的应用任务范式分析与展望

doi: 10.11999/JEIT221459
基金项目: 科技创新2030新一代人工智能项目(2020AAA0104305),中国科学院先导专项(XDA27010000, XDB32070000),上海市市级科技重大专项(2021SHZDZX),中国科学院青年促进会
详细信息
    作者简介:

    张铁林:男,博士,副研究员,研究方向为类脑脉冲神经网络、脑机接口等

    李澄宇:男,博士,研究员,研究方向为工作记忆的神经环路机制

    王刚:男,博士,副研究员,研究方向为类脑视觉感知

    张马路:男,博士,研究员,研究方向为类脑智能、机器学习等

    余磊:男,博士,副教授,博士生导师,研究方向为信号处理、图像处理、类脑视觉成像与感知等

    徐波:男,博士,研究员,研究方向为类脑智能、决策博弈、语音识别等

    通讯作者:

    张铁林 tielin.zhang@ia.ac.cn

  • 中图分类号: TP183

Research Advances and New Paradigms for Biology-inspired Spiking Neural Networks

Funds: The National Key Research and Development Program of China (2020AAA0104305), The Strategic Priority Research Program of the Chinese Academy of Sciences (XDA27010000, XDB32070000), The Shanghai Municipal Science and Technology Major Project (2021SHZDZX), and The Youth Innovation Promotion Association CAS
  • 摘要: 类脑脉冲神经网络(SNN)由于同时具有生物合理性和计算高效性等特点,因而在生物模拟计算和人工智能应用两个方向都受到了广泛关注。该文通过对SNN发展历史演进的分析,发现上述两个原本相对独立的研究方向正在朝向快速交叉融合的趋势发展。回顾历史,动态异步事件信息采集装置的成熟,如动态视觉相机(DVS)、动态音频传感(DAS)的成功应用,使得SNN可以有机会充分发挥其在脉冲时空编码、神经元异质性、功能环路特异性、多尺度可塑性等方面的优势,并在一些传统典型的应用任务中崭露头角,如动态视觉信号追踪、听觉信息处理、强化学习连续控制等。与这些物理世界的应用任务范式相比,生物大脑内部存在着一个特殊的生物脉冲世界,这个脉冲世界与外界物理世界互为映像且复杂度相似。展望未来,随着侵入式、高通量脑机接口设备的逐步成熟,脑内脉冲序列的在线识别和反向控制,将逐渐成为一个天然适合SNN最大化发挥其低能耗、鲁棒性、灵活性等优势的新型任务范式。类脑SNN从生物启发而来,并将最终应用到生物机制探索中去,相信这类正反馈式的科研方式将极大的加速后续相关的脑科学和类脑智能研究。
  • 图  1  SNN历史发展时间简表

    图  2  SNN和ANN的关键特性比较

    图  3  SNN和ANN的卷积计算比较

    图  4  基于事件的图像运动模糊重建和图像插帧原理示意图[44,46]

    图  5  一种生物视觉双通路启发的类脑ANN-SNN视觉感知应用示例[48]

    图  6  一种结合脉冲Transformer和多类动力学脉冲神经元的SNN听觉信息处理示例[52]

    图  7  一种脉冲策略网络为主、人工评估网络为辅的强化学习连续控制应用示例[21]

    图  8  高通量侵入式脑机接口脉冲序列作为外部世界的复杂映像

    图  9  两种典型模式动物的生物实验范式示例及对应的脉冲序列数据集

    图  10  融合生物和人工网络优势的SNN模型天然适合动态视听觉和脑机接口两类典型范式

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  • 收稿日期:  2022-11-21
  • 修回日期:  2023-05-08
  • 网络出版日期:  2023-05-10
  • 刊出日期:  2023-08-21

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