计算机科学 ›› 2023, Vol. 50 ›› Issue (2): 364-373.doi: 10.11896/jsjkx.220500023

• 交叉&前沿 • 上一篇    下一篇

类脑心智计算的科学技术和工程应用的研究与思考

刘扬1,4,7, 刘睿佳4,5, 周黎鸣1,4, 左宪禹2,4, 杨伟2,4, 周毅3,6,7   

  1. 1 河南大学河南省空间信息处理工程研究中心 河南 开封 475004
    2 河南大学河南省大数据分析与处理重点实验室 河南 开封 475004
    3 河南大学河南省车联网协同技术国际联合实验室 郑州 450046
    4 河南大学计算机与信息工程学院 河南 开封 475004
    5 河南大学软件学院 河南 开封 475004
    6 河南大学人工智能学院 郑州 450046
    7 河南大学深圳研究院 广东 深圳 518000
  • 收稿日期:2022-05-05 修回日期:2022-08-26 出版日期:2023-02-15 发布日期:2023-02-22
  • 通讯作者: 周黎鸣(lmzhou@henu.edu.cn)
  • 作者简介:(ly.sci.art@gmail.com)
  • 基金资助:
    国家自然科学基金(62176087,62176088,61806074);深圳市中央引导地方科技发展专项(2021Szvup032,2021Szvup029);河南省研究生教育改革与质量提升工程项目(YJS2022JC33);河南大学教学改革研究与实践项目(HDXJJG2020-109,HDXJJG2019-81,HDXJJG2020-74)

Thoughts on Development and Research of Science,Technology and Engineering Application of Brain & Mind-inspired Computing

LIU Yang1,4,7, LIU Ruijia4,5, ZHOU Liming1,4, ZUO Xianyu2,4, YANG Wei2,4, ZHOU Yi3,6,7   

  1. 1 Henan Province Engineering Research Center of Spatial Information Processing,Henan University,Kaifeng,Henan 475004,China
    2 Henan Key Laboratory of Big Data Analysis and Processing,Henan University,Kaifeng,Henan 475004,China
    3 International Joint Research Laboratory for Cooperative Vehicular Networks of Henan,Henan University,Zhengzhou 450046,China
    4 School of Computer and Information Engineering,Henan University,Kaifeng,Henan 475004,China
    5 School of Software,Henan University,Kaifeng,Henan 475004,China
    6 School of Artificial Intelligence,Henan University,Zhengzhou 450046,China
    7 Shenzhen Research Institute,Henan University,Shenzhen,Guangdong 518000,China
  • Received:2022-05-05 Revised:2022-08-26 Online:2023-02-15 Published:2023-02-22
  • Supported by:
    National Natural Science Foundation of China(62176087,62176088,61806074),Shenzhen Special Foundation of Central Government to Guide Local Science & Technology Development(2021Szvup032,2021Szvup029),Postgraduate Education Reform and Quality Improvement Project of Henan Province(YJS2022JC33) and Education Reform Research and Practice Project of Henan University(HDXJJG2020-109,HDXJJG2019-81,HDXJJG2020-74).

摘要: 发展新一代的类脑智能,需要综合考虑形成自然智能的结构、功能和行为等研究,偏颇任一方向都是不全面的,难以完全触及智能的本质。文中基于神经系统的结构仿真、认知系统的功能模仿和自然智能的行为模拟,定义了类脑心智计算(BMC)的基本概念,提出了BMC的假设、模型和框架,研究了BMC的前沿理论。在大脑机制、心智模式和行为控制上,分析了当前BMC研究的技术路线、核心算法和关键技术,综述了BMC的复杂系统和工程应用现状。结合智能科学、神经科学、认知科学、信息科学和计算数学等多学科的交叉融合特征,进一步讨论了BMC的科研范式和跨学科建设问题。BMC研究将有望在新一代类脑智能的科学理论、技术创新和工程系统上取得重大突破。

关键词: 类脑心智计算, 类脑智能, 跨媒体认知神经计算, 跨模态神经认知计算, 跨学科研究

Abstract: To develop a new generation of brain-inspired intelligence,we need to comprehensively consider the structure,function and behavior of natural intelligence.Bias in any direction is not comprehensive,and it is difficult to fully touch the essence of intelligence.Based on the structure simulation of nervous system,the function emulation of cognitive system and the behavior imitation of natural intelligence,this paper defines the basic concept of brain & mind-inspired computing(BMC),puts forward the hypothesis,model and framework of BMC,and studies the frontier theory of BMC.Then it explores and analyzes the technical route,core algorithms and key technologies of BMC research,and summarizes the current situation of complex system and engineering application of BMC in the aspects of brain mechanism,mental model and behavior control.Combined with the multidisciplinary and interdisciplinary characteristics of intelligence science,neuroscience,cognitive science,information science and computational mathematics,it further discusses the research paradigm and transdisciplinary construction of BMC,brain-inspired computing and brain-like computing.Reserch of BMC is expected to make a major breakthrough in the scientific theory,technological innovation and engineering system of the new generation of brain-inspired intelligence.

Key words: Brain and mind inspired computing, Brain-inspired intelligence, Cross-media cognitive neural computing, Cross-modal neural cognitive computing, Interdisciplinary research

中图分类号: 

  • TP183
[1]LI D Y.Ten questions and answers for the new generation of artificial intelligences[J].CAAI Transactions on Intelligent Systems,2021,16(5):828-833.
[2]ASAKIEWICZ C,STOHR E A,MAHAJAN S,et al.Building a Cognitive Application Using Watson DeepQA [J].IT Professional,2017,19(4):36-44.
[3]SILVER D,HUANG A,MADDISON C J,et al.Mastering the game of Go with deep neural networks and tree search [J].Nature,2016,529(7587):484-489.
[4]SILVER D,SCHRITTWIESER J,SIMONYAN K,et al.Mastering the game of Go without human knowledge [J].Nature,2017,550(7676):354-359.
[5]SILVER D,HUBERT T,SCHRITTWIESER J,et al.A general reinforcement learning algorithm that masters chess,shogi,and Go through self-play [J].Science,2018,362(6419):1140-1144.
[6]POO M M,XU B,TAN T N.Brain science and brain-inspiredintelligence technology-an overview[J].Bulletin of the Chinese Academy of Sciences,2016,31(7):725-736,714.
[7]AMUNTS K,EBELL C,MULLER J,et al.The Human Brain Project:Creating a European Research Infrastructure to Decode the Human Brain [J].Neuron,2016,92(3):574-581.
[8]BARGMANN C I,NEWSOME W T.The Brain ResearchThrough Advancing Innovative Neurotechnologies(BRAIN) Initiative and Neurology [J].JAMA Neurology,2014,71(6):675-676.
[9]OKANO H,SASAKI E,YAMAMORI T,et al.Brain/MINDS:A Japanese National Brain Project for Marmoset Neuroscience [J].Neuron,2016,92(3):582-590.
[10]POO M M,DU J L,IP N Y,et al.China Brain Project:Basicneuroscience,brain diseases,and brain-inspired computing [J].Neuron,2016,92(3):591-596.
[11]LIU Y,HU D.High performance computing in the viewpoint of brain research [J].Chinese Journal of Computers,2017,40(9):2148-2166.
[12]XU B,LIU C L,ZENG Y.Research status and developments of brain-inspired intelligence [J].Bulletin of the Chinese Academy of Sciences,2016,31(7):793-802.
[13]DENG L.Towards the computational model and key technologies on heterogeneous brain-inspired computing platform [D].Beijing:Tsinghua University,2017.
[14]SHI L P,PEI J,ZHAO R.Brain-inspired computing for artificial general intelligence [J].Artificial Intelligence,2020,1:6-15.
[15]YANG S M,HAO X Y,WANG J,et al.Large-scale brain-in-spired computing system BiCoSS:Its architecture,implementation and application[J].Acta Automatica Sinica,2021,47(9):2154-2169.
[16]ZHONG Y X.Mechanism:a unified theory of AI [J].Acta Electronica Sinica,2006,34(2):317-321.
[17]LIU Y.Target recognition of high resolution remote sensingimage based on multi-media neural cognitive computing model [D].Kaifeng:Henan University,2016.
[18]LIU Y,FU Z Y,ZHENG F B.Scene classification of high-resolution remote sensing image based on multimedia neural cognitive computing [J].Systems Engineering and Electronics,2015,37(11):2623-2633.
[19]LIU Y,YANG W,ZHENG F B.Cognitive neural mechanisms and saliency computational model of visual selective attention [J].Journal of Chinese Computer Systems,2014,35(3):584-589.
[20]LIU Y,ZHANG M H,ZHENG F B.Cognitive neural mechanisms and saliency computational model of auditory selective attention [J].Computer Science,2013,40(6):283-287.
[21]LIU Y,CAI K,LIU C,et al.CSRNCVA:A model of cross-media semantic retrieval based on neural computing of visual and auditory sensations [J].Neural Network World,2018,28(4):305-323.
[22]LIU Y,TU C L,ZHENG F B.Research of neural cognitive computing model for visual and auditory cross-media retrieval[J].Computer Science,2015,42(3):19-25,30.
[23]LIU Y,XIE Y,YANG W,et al.Target Classification and Recognition for High-Resolution Remote Sensing Images:Using the Parallel Cross-Modal Neural Cognitive Computing Algorithm [J].IEEE Geoscience and Remote Sensing Magazine,2020,8(3):50-62.
[24]LIU Y,ZHENG F B,ZUO X.CSMCCVA:Framework of cross-modal semantic mapping based on cognitive computing of visual and auditory sensations [J].High Technology Letters,2016,22(1):90-98.
[25]LIU Y,ZHENG F B.Object-oriented and Multi-scale TargetClassification and Recognition Based on Hierarchical Ensemble Learning [J].Computers and Electrical Engineering,2017,62(2017):538-554.
[26]LIU Y,ZHENG F B.JIANG B Q,et al.Research of cross-media information retrieval model based on multimodal fusion and temporal-spatial context semantic[J].Journal of Computer Applications,2009,29(4):1182-1187.
[27]LIU Y.Research and application of cross-media retrieval model based on temporal-spatial correlation [D].Kaifeng:Henan University,2009.
[28]SHAO Y X,MENG W,KONG D Z,et al.Cross-modal retrieval method for special vehicles based on deep learning [J].Compu-ter Science,2020,47(12):205-209.
[29]LIU Y,ZHENG F B,FAN B L.TV news automatic segmentation base on text and audio-visual multimodal features information[J].Computer Engineering and Applications,2007,43(35):190-194.
[30]PICCININI G.The First Computational Theory of Mind andBrain:A Close Look at Mcculloch and Pitts's “Logical Calculus of Ideas Immanent in Nervous Activity” [J].Synthese,2004,141(2):175-215.
[31]JIAO L C,YANG S Y,LIU F.Seventy years beyond neural networks:retrospect and prospect [J].Chinese Journal of Compu-ters,2016,39(8):1697-1716.
[32]LECUN Y,BENGIO Y,HINTON G.Deep learning [J].Nature,2015,521(7553):436-444.
[33]FUKUSHIMA K,SHOUNO H.Deep Convolutional Network Neocognitron:Improved Interpolating-Vector [C]//2015 International Joint Conference on Neural Networks(IJCNN).Killarney:IEEE,2015:1-8.
[34]KRIZHEVSKY A,SUTSKEVER I,HINTON G.Imagenetclassification with deep convolutional neural networks [C]//Advances in Neural Information Processing Systems.2012:1106-1114.
[35]SIMONYAN K,ZISSERMAN A.Very Deep Convolutional Networks for Large-Scale Image Recognition [C]//Computer Vision and Pattern Recognition.2014.
[36]SZEGEDY C,LIU W,JIA Y,et al.Going deeper with convolutions [C]//2015 IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Boston,MA,2015:1-9.
[37]HE K,ZHANG X,REN S,et al.Identity Mappings in Deep Residual Networks [C]//2016 European Conference on Computer Vision(ECCV).Springer International Publishing,2016.
[38]SABOUR S,FROSST N,HINTON G E.Dynamic Routing Between Capsules [C]//Neural Information Processing Systems.2017:3856-3866.
[39]CRESWELL A,WHITE T,DUMOULIN V,et al.GenerativeAdversarial Networks:An Overview [J].IEEE Signal Proces-sing Magazine,2018,35(1):53-65.
[40]HUANG T J,YU Z F,LIU Y J.Brain-like machine:Thought and architecture [J].Journal of Computer Research and Deve-lopment,2019,56(6):1135-1148.
[41]QU P,CHEN J J,ZHANG Y H.A proposal of software-hardware decoupling hardware design method for brain-inspired computing [J].Journal of Computer Research and Development,2021,58(6):1146-1154.
[42]MEAD C.Neuromorphic electronic systems [C]//Proceedings of the IEEE.1990:1629-1636.
[43]CHEN Y,LI H,WU C,et al.Neuromorphic computing's yesterday,today,and tomorrow - an evolutional view [J].Integration,2018,61(2018):49-61.
[44]ZHOU E,FANG L,LIU R,et al.An improved memristor model for brain-inspired computing[J].Chinese Physics B,2017,26(11):537-543.
[45]WANG X,LIN X,DANG X.Supervised learning in spiking neural networks:A review of algorithms and evaluations [J].Neural Networks,2020,125(2020):258-280.
[46]HU Y F,LI G Q,WU Y J.Spiking neural networks A survey on recent advances and new directions [J].Control and Decision,2021,36(1):1-26.
[47]ZHANG H G.XU G Z.GUO J R.A review of brain-like spiking neural network and its neuromorphic chip research [J].Journal of Biomedical Engineering,2021,38(5):986-994,1002.
[48]ZHANG T L,XU B.Research advances and perspectives on spiking neural networks [J].Chinese Journal of Computers,2021,44(9):1767-1785.
[49]OLIVEIRA L D R,GOMES R M,SANTOS B A,et al.Effects of the parameters on the oscillation frequency of Izhikevich spiking neural networks [J].Neurocomputing,2019,337(APR.14):251-261.
[50]PONULAK F,KASIN'SKI A.Supervised Learning in Spiking Neural Networks with ReSuMe:Sequence Learning,Classification,and Spike Shifting [J].Neural Computation,2010,22(2):467-510.
[51]GTIG R,SOMPOLINSKY H.The tempotron:a neuron thatlearns spike timing-based decisions [J].Nature Neuroscience,2006,9(3):420-428.
[52]BOHTE S M,KOK J N,HAN L P.Error-backpropagation in temporally encoded networks of spiking neurons [J].Neurocomputing,2002,48(1/2/3/4):17-37.
[53]ZENG Y,ZHANG T,XU B.Improving multi-layer spiking neural networks by incorporating brain-inspired rules [J].Science China-Information Sciences,2017,60(5):222-232.
[54]JANG H,SKATCHKOVSKY N,SIMEONE O.Spiking Neural Networks-Part I:Detecting Spatial Patterns [J].IEEE Communications Letters,2021,25(6):1736-1740.
[55]SKATCHKOVSKY N,JANG H,SIMEONE O.Spiking Neural Networks-Part II:Detecting Spatio-Temporal Patterns[J].IEEE Communications Letters,2021,25(6):1741-1745.
[56]SKATCHKOVSKY N,JANG H,SIMEONE O.Spiking Neural Networks-Part III:Neuromorphic Communications[J].IEEE Communications Letters,2021,25(6):1746-1750.
[57]PAUL A M.Artificial brains.A million spiking-neuron integrated circuit with a scalable communication network and interface [J].Science,2014,6197(345):668-673.
[58]DAVIES M,SRINIVASA N,LIN T,et al.Loihi:A Neuromorphic Manycore Processor with On-Chip Learning [J].IEEE Micro,2018,38(1):82-99.
[59]JOUPPI N P,YOUNG C,PATIL N,et al.Motivation for andEvaluation of the First Tensor Processing Unit [J].IEEE Micro,2018,38(3):10-19.
[60]KHODAGHOLY D,GELINAS J N,THESEN T,et al.NeuroGrid:recording action potentials from the surface of the brain [J].Nature Neuroscience,2015,18(2):310-315.
[61]GRUBL A,BILLAUDELLE S,CRAMER B,et al.Verificationand Design Methods for the BrainScaleS Neuromorphic Hardware System [J].Journal of Signal Processing Systems for Signal Image and Video Technology,2020,92(2020):1277-1292.
[62]MIKAITIS M,GARCIA G P,KNIGHT J C,et al.Neuromodulated Synaptic Plasticity on the SpiNNaker Neuromorphic System [J].Frontiers in Neuroscience,2018,12(2018):1-13.
[63]SHEN J,MA D,GU Z,et al.Darwin:a Neuromorphic Hardware Co-Processor based on Spiking Neural Networks [J].Science China-Information Sciences,2016,59(2):43-51.
[64]PEI J,DENG L,SONG S,et al.Towards artificial general intelligence with hybrid Tianjic chip architecture [J].Nature,2019,572(7767):106-111.
[65]YAO P,WU H,GAO B,et al.Fully hardware-implementedmemristor convolutional neural network [J].Nature,2020,577(7792):641-646.
[66]LUO T,LIU S L,LI L,et al.DaDianNao:A Neural Network Supercomputer [J].IEEE Transactions on Computers,2017,66(1):73-88.
[67]ELIASMITH C,STEWART T C,CHOO X,et al.A large-scale model of the functioning brain [J].Science,2012,338(6111):1202-1205.
[68]ZHANG Y,QU P,JI Y,et al.A system hierarchy for brain-inspired computing [J].Nature,2020,586(7829):378-384.
[69]DELORME A,THORPE S J.SpikeNET:an event-driven simulation package for modelling large networks of spiking neurons [J].Network-Computation in Neural Systems,2003,14(4):613-627.
[70]KUMBHAR P,HINES M,FOURIAUX J,et al.CoreNEU-RON:An Optimized Compute Engine for the NEURON Simulator [J].Frontiers in Neuroinformatics,2019,13(2019):1-16.
[71]MIGLIORE M,CANNIA C,LYTTON W W,et al.Parallel network simulations with NEURON [J].Journal of Computational Neuroscience,2006,21(2):119-129.
[72]CRONE J C,VINDIOLA M M,YU A B,et al.Enabling Large-Scale Simulations With the GENESIS Neuronal Simulator[J].Frontiers in Neuroinformatics,2019,13(2019):1-12.
[73]GOODMAN D,BRETTE R.Brian:a simulator for spiking neural networks in python [J].Frontiers in Neuroinformatics,2008,2(2008):1-2.
[74]STIMBERG M,GOODMAN D F M,BENICHOUX V,et al.Brian 2 - the second coming:spiking neural network simulation in Python with code generation [J].BMC Neuroscience,2013,14(1):37-38.
[75]ZAYTSEV Y V,MORRISON A.CyNEST:a maintainable Cython-based interface for the NEST simulator [J].Frontiers in Neuroinformatics,2014,8(2014):1-10.
[76]LIU Y,CAI K,ZHANG M,et al.Target Detection in Remote Sensing Image Based on Saliency Computation of Spiking Neural Network [C]//38th Annual IEEE International Geoscience and Remote Sensing Symposium(IGARSS 2018),Valencia,Spain.Institute of Electrical and Electronics Engineers Inc.,2018:2865-2868.
[77]LIU Y,ZHANG M,XU P,et al.SAR Ship Detection Using Sea-land Segmentation-based Convolutional Neural Network [C]//
2017 International Workshop on Remote Sensing with Intelli-gent Processing(RSIP 2017),Shanghai,China.Institute of Electrical and Electronics Engineers Inc.,2017:1-4.
[78]LIU Y,FU Z Y,ZHENG F B.Review on high resolution remote sensing image classification and recognition [J].Journal of Geo-Information Science,2015,17(9):1080-1091.
[79]LIU Y.ZUO X Y.The thoughts on interdisciplinary research of multi-media neural cognitive computing [J].Computer Education,2014,23:48-52.
[80]JIAO L C.Challenges and thinking of the next generation of artificial intelligence[J].CAAI Transactions on Intelligent Systems,2020,15(6):1185-1187.
[1] 周明强, 代开浪, 吴全旺, 朱庆生.
异构信息网络的注意力感知多通道图卷积评分预测模型
Attention-aware Multi-channel Graph Convolutional Rating Prediction Model for Heterogeneous Information Networks
计算机科学, 2023, 50(3): 129-138. https://doi.org/10.11896/jsjkx.220300004
[2] 胡中源, 薛羽, 查加杰.
演化循环神经网络研究综述
Survey on Evolutionary Recurrent Neural Networks
计算机科学, 2023, 50(3): 254-265. https://doi.org/10.11896/jsjkx.220600007
[3] 王鹏宇, 台文鑫, 刘芳, 钟婷, 罗绪成, 周帆.
基于数据增强的自监督飞行航迹预测
Self-supervised Flight Trajectory Prediction Based on Data Augmentation
计算机科学, 2023, 50(2): 130-137. https://doi.org/10.11896/jsjkx.211200016
[4] 黄泽南, 刘晓捷, 赵晨晖, 邓亚彬, 郭东辉.
类脑计算脉冲神经网络模型及其学习算法研究进展
Spiking Neural Network Model for Brain-like Computing and Progress of Its Learning Algorithm
计算机科学, 2023, 50(1): 229-242. https://doi.org/10.11896/jsjkx.220100058
[5] 张宇欣, 陈益强.
基于多尺度特征融合的驾驶员注意力分散检测方法
Driver Distraction Detection Based on Multi-scale Feature Fusion Network
计算机科学, 2022, 49(11): 170-178. https://doi.org/10.11896/jsjkx.211000040
[6] 宋美琦, 傅湘玲, 闫晨巍, 仵伟强, 任芸.
基于双向长短时记忆网络的企业弹性能力预测模型
Prediction Model of Enterprise Resilience Based on Bi-directional Long Short-term Memory Network
计算机科学, 2022, 49(11): 197-205. https://doi.org/10.11896/jsjkx.210900195
[7] 徐晖, 王中卿, 李寿山, 张民.
结合情感信息的个性化对话生成
Personalized Dialogue Generation Integrating Sentimental Information
计算机科学, 2022, 49(11A): 211100019-6. https://doi.org/10.11896/jsjkx.211100019
[8] 钱文祥, 衣杨.
视频识别深度学习网络综述
Survey of Deep Learning Networks for Video Recognition
计算机科学, 2022, 49(11A): 211200025-10. https://doi.org/10.11896/jsjkx.211200025
[9] 顾曦龙, 宫宁生, 胡乾生.
基于YOLOv3与改进VGGNet的车辆多标签实时识别算法
Multi-label Vehicle Real-time Recognition Algorithm Based on YOLOv3 and Improved VGGNet
计算机科学, 2022, 49(11A): 210600142-7. https://doi.org/10.11896/jsjkx.210600142
[10] 车爱博, 张辉, 李晨, 王耀南.
基于点云数据的交通环境下单阶段三维目标检测方法
Single-stage 3D Object Detector in Traffic Environment Based on Point Cloud Data
计算机科学, 2022, 49(11A): 210900079-6. https://doi.org/10.11896/jsjkx.210900079
[11] 钱栋炜, 崔阳光, 魏同权.
基于深度神经网络与联邦学习的污染物浓度预测二次建模
Secondary Modeling of Pollutant Concentration Prediction Based on Deep Neural Networks with Federal Learning
计算机科学, 2022, 49(11A): 211200084-5. https://doi.org/10.11896/jsjkx.211200084
[12] 方仲俊, 张静, 李冬冬.
基于空间和多层级联合编码的图像描述算法
Spatial Encoding and Multi-layer Joint Encoding Enhanced Transformer for Image Captioning
计算机科学, 2022, 49(10): 151-158. https://doi.org/10.11896/jsjkx.210900159
[13] 方阳, 赵婷, 刘期烈, 贺侗, 孙开伟, 陈前斌.
基于图交互与场景感知融合的轨迹预测方法
Trajectory Prediction Method Based on Fusion of Graph Interaction and Scene Perception
计算机科学, 2022, 49(10): 258-264. https://doi.org/10.11896/jsjkx.211000172
[14] 杨文博, 原继东.
局部时间序列黑盒对抗攻击
Locally Black-box Adversarial Attack on Time Series
计算机科学, 2022, 49(10): 285-290. https://doi.org/10.11896/jsjkx.210900254
[15] 王冠宇, 钟婷, 冯宇, 周帆.
基于矢量量化编码的协同过滤推荐方法
Collaborative Filtering Recommendation Method Based on Vector Quantization Coding
计算机科学, 2022, 49(9): 48-54. https://doi.org/10.11896/jsjkx.210700109
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!