摘要类脑智能是受大脑神经运行机制和认知行为机制启发,以计算建模为手段,通过软硬件协同实现的机器智能,具备信息处理机制上类脑、认知行为表现上类人、智能水平上达到或超越人的特点,近年来受到了各国关注。以“类脑智能”为主题,对Web of Science数据库中2010—2019年的相关文献进行检索,利用CiteSpace软件进行可视化分析,分析类脑智能研究的发文趋势、代表文献、研究热点及前沿,并对类脑智能领域的研究热点和研究前沿进行阐述和展望。
Abstract：Inspired by the brain's neural operation and cognitive behavior mechanisms, the brain-inspired intelligence uses computational modeling as a means to achieve machine intelligence through hardware and software collaboration with characteristics of brain-like information processing mechanism, human-like cognitive behavior, and human intelligence or more, attracting more and more attention. From the point of co-citation and co-words, articles about the research of brain-inspired intelligence between 2010 and 2019 were retrieved in Web of Science and CiteSpace software was used to evaluate the global scientific output. Research hotspots and trends, that is, the construction of brain-like neural network computing models and learning methods with the help of brain science and the in-depth and cross-study of brain-computer interface and deep learning, were discussed in detail.
刘洁, 吴慧. 类脑智能研究热点及趋势[J]. 中国生物医学工程学报, 2021, 40(1): 91-98.
Liu Jie, Wu Hui. Research Hotspots and Trends of Brain-Inspired Intelligence. Chinese Journal of Biomedical Engineering, 2021, 40(1): 91-98.
[1] 蒲慕明,徐波,谭铁牛. 脑科学与类脑研究概述[J]. 中国科学院院刊,2016,31(7):725-736. [2] 全球工程前沿项目组. 全球工程前沿2019[EB/OL]. http://www.engineering.org.cn/ch/con/Engineering-Fronts/archive, 2019-12-12/2020-02-06. [3] 曾毅,刘成林,谭铁牛. 类脑智能研究的回顾与展望[J]. 计算机学报,2016,39(1):212-222. [4] Chen Chaomei. CiteSpace: Visualizing patterns and trends in scientific literature [EB/OL]. http://cluster.cis.drexel.edu/～cchen/citespace/download/, 2019-10-25/2020-01-31. [5] 李杰,陈超美. CiteSpace科技文本挖掘及可视化[M]. 北京:首都经济贸易大学出版社,2016. [6] 陈悦,陈超美,刘则渊,等. CiteSpace知识图谱的方法论功能[J]. 科学学研究,2015,33(2):242-253. [7] Ganguly K, Carmena JM. Emergence of a stable cortical map for neuroprosthetic control[J]. PLOS Biology,21 Jul, 2009,7 [Epub ahead of print]. [8] Ahmad AS, Sumari ADW. Cognitive artificial intelligence: Brain-inspired intelligent computation in artificial intelligence[C] // 2017 Computing Conference. London: IEEE, 2017:135-141. [9] Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017,60(6):84-90. [10] Merolla PA, Arthur JV, Alvarez-Icaza R, et al. A million spiking-neuron integrated circuit with a scalable communication network and interface[J]. Science,2014,345(6197):668-673. [11] Fairhall A, Machens C. Editorial overview: Computational neuroscience[J]. Current Opinion in Neurobiology, 2017,46:A1-A5. [12] Chen Chaomei. CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature[J]. Journal of the American Society for Information Science and Technology, 2006,57(3):359-377. [13] Velliste M, Perel S, Spalding MC, et al. Cortical control of a prosthetic arm for self-feeding[J]. Nature,2008,453(7198):1098-1101. [14] Moritz CT, Perlmutter SI, Fetz EE. Direct control of paralysed muscles by cortical neurons[J]. Nature,2008,456(7222):639-663. [15] Ramos-Murguialday A, Broetz D, Rea M, et al. Brain-machine interface in chronic stroke rehabilitation: A controlled study[J]. Annals of Neurology,2013,74(1):100-108. [16] Blankertz B, Tomioka R, Lemm S, et al. Optimizing spatial filters for robust EEG single-trial analysis[J]. IEEE Signal Processing Magazine, 2008,25(1):41-56. [17] Pereira F, Mitchell T, Botvinick M. Machine learning classifiers and fMRI: A tutorial overview[J]. NeuroImage,2009,451(1):S199-S209. [18] Hochberg LR, Bacher D, Jarosiewicz B, et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm[J]. Nature, 2012,485(7398):372-375. [19] Nijboer F, Sellers EW, Mellinger J, et al. A P300-based brain-computer interface for people with amyotrophic lateral sclerosis[J]. Clinical Neurophysiology, 2008,119(8):1909-1916. [20] Lecun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015,521(7553):436-444. [21] Oostenveld R, Fries P, Maris E, et al. FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data[J]. Computational Intelligence and Neuroscience, 23 Dec, 2011[Epub ahead of print]. [22] Arbabshirani MR, Plis S, Sui J, et al. Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls[J]. NeuroImage, 2017,145(SIB):137-165. [23] Gilja V, Nuyujukian P, Chestek CA, et al. A high-performance neural prosthesis enabled by control algorithm design[J]. Nature Neuroscience, 2012,15(12):1752-1757. [24] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation[C] //Lecture Notes in Computer Science. Munich: Springer-Verlag, 2015: 9351,234-241. [25] Haufe S, Meinecke F, Goergen K, et al. On the interpretation of weight vectors of linear models in multivariate neuroimaging[J]. NeuroImage, 2014,87:96-110. [26] Wolpaw JR, Birbaumer N, Heetderks WJ, et al. Brain-computer interface technology: A review of the first international meeting[J]. IEEE Transactions on Rehabilitation Engineering,2000,8(2):164-173. [27] Presacco A, Forrester L, Contreras-Vidal JL. Towards a non-invasive brain-machine interface system to restore gait function in humans[C]// 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Boston: IEEE,2011:4588-4591. [28] Qamar S, Jin H, Zheng R, et al. Multi stream 3D hyper-densely connected network for multi modality isointense infant brain MRI segmentation[J]. Multimedia Tools and Applications, 2019, 78(18):25807-25828. [29] Vyas S, Even-Chen N, Stavisky SD, et al. Neural population dynamics underlying motor learning transfer [J]. Neuron, 2018,97(5):998-1000. [30] Amin SU, Alsulaiman M, Muhammad G, et al. Multilevel weighted feature fusion using convolutional neural networks for EEG motor imagery classification[J]. IEEE Access, 2019,7: 18940-18950. [31] Acharya UR, Oh SL, Hagiwara Y, et al. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals[J]. Computers in Biology and Medicine, 2018,100:270-278. [32] Alban F, Gegenfurtner KR. Processing of chromatic information in a deep convolutional neural network[J]. Journal of the Optical Society of America A- Optics Image Science and Vision, 2018, 35(4): B334-B346. [33] Chen Yicheng, Villanueva-Meyer JE, Morrison MA, et al. Toward automatic detection of radiation-induced cerebral microbleeds using a 3D deep residual network[J]. Journal of Digital Imaging, 2019,32(5):766-772. [34] Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: Clinical challenges and applications [J]. CA-A Cancer Journal for Clinicians, 2019, 69(2):127-157. [35] Lu Huimin, Li Yujie, Chen Min, et al. Brain intelligence: Go beyond artificial intelligence[J]. Mobile Networks and Applications, 2018, 23(2):368-375. [36] Yao Dezhong, Zhang Yangsong, Liu Tiejun, et al. Bacomics: A comprehensive cross area originating in the studies of various brain-apparatus conversations [J]. Cognitive Neurodynamics, Mar 2020 [Epub ahead of print]. [37] Erboz G. How to define Industry 4.0: Main pillars of Industry 4.0 [C]// Managerial Trends in the Development of Enterprises in Globalization Era. Nitra: Slovak Univ Agriculture Nitra, 2017: 761-767. [38] Deloitte. The future awakens [EB/OL]. https://www2.deloitte.com/cn/zh/pages/technology-media-and-telecommunications/articles/global-ai-development-white-paper.html, 2019-08-30/2020-04-20. [39] 中国神经科学学会神经科学方向预测及技术路线图研究项目组. 脑科学发展态势及技术预见[J]. 科技导报,2018,36(10):6-13.
