适合类脑脉冲神经网络的应用任务范式分析与展望  

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

作　　者：张铁林[1] 李澄宇[2] 王刚 张马路 余磊[5] 徐波[1] ZHANG Tielin;LI Chengyu;WANG Gang;ZHANG Malu;YU Lei;XU Bo(Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China;Institute of Neuroscience,Chinese Academy of Sciences,Shanghai 200031,China;Center of Brain Sciences,Beijing Institute of Basic Medical Sciences,Beijing 100850,China;University of Electronic Science and Technology of China,Chengdu 611731,China;Wuhan University,Wuhan 430072,China)

机构地区：[1]中国科学院自动化研究所,北京100190 [2]中国科学院神经科学研究所,上海200031 [3]军事科学院军事认知与脑科学研究所,北京100850 [4]电子科技大学,成都611731 [5]武汉大学,武汉430072

出　　处：《电子与信息学报》2023年第8期2675-2688,共14页Journal of Electronics & Information Technology

基　　金：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),中国科学院青年促进会;上海市市级科技重大专项(2021SHZDZX)。

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。Spiking Neural Networks(SNN)are gaining popularity in the computational simulation and artificial intelligence fields owing to their biological plausibility and computational efficiency.Herein,the historical development of SNN are analyzed to conclude that these two fields are intersecting and merging rapidly.After the successful application of Dynamic Vision Sensors(DVS)and Dynamic Audio Sensors(DAS),SNNs have found some proper paradigms,such as continuous visual signal tracking,automatic speech recognition,and reinforcement learning of continuous control,that have extensively supported their key features,including spiking encoding,neuronal heterogeneity,specific functional circuits,and multiscale plasticity.In comparison to these real-world paradigms,the brain contains a spiked version of the biology-world paradigm,which exhibits a similar level of complexity and is usually considered a mirror of the real world.Considering the projected rapid development of invasive and parallel Brain-computer Interface(BCI),as well as the new BCI-based paradigm,which includes online pattern recognition and stimulus control of biological spike trains,it is natural for SNNs to exhibit their key advantages of energy efficiency,robustness,and flexibility.The biological brain has inspired the present study of SNNs and effective SNN machine-learning algorithms,which can help enhance neuroscience discoveries in the brain by applying them to the new BCI paradigm.Such two-way interactions with positive feedback can accelerate brain science research and brain-inspired intelligence technology.

关 键 词：脉冲神经网络(SNN) 类脑智能 脑机接口(BCI) 实验范式 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]