王笑;洪朝飞;张宇豪;吴迅冬;唐华锦;

• 1:之江实验室智能计算研究院智能计算硬件研究中心
• 2:浙江大学计算机科学与技术学院

摘要(Abstract)：

过去近百年的神经科学研究让我们对大脑的工作机制取得了初步的理解，脑仿真技术在其中发挥了关键性的作用。未来脑仿真研究将在理解大脑工作机制、推进类脑计算与新一代人工智能的发展中发挥重要作用。我们在这篇综述中，围绕面向类脑计算的脑仿真研究进展情况，从模型、仿真工具、皮层和神经环路功能模拟、脑疾病、与人工智能的互相促进等几方面进行阐述与分析，并对其未来发展趋势及有望取得突破的一些关键问题进行了展望，希望能够促进对类脑计算、脑仿真，以及人工智能交叉领域的研究兴趣。

关键词(KeyWords)： 类脑计算;脑仿真;脑模拟;神经网络;深度学习;人工智能;机器学习

Abstract:

Keywords:

基金项目(Foundation): 国家重点研发计划（2020AAA0105900）;; 国家自然科学基金（62236007,62076084,62106234）;; 之江实验室科研攻关项目资助（2021KC0AC01）

作者(Authors): 王笑;洪朝飞;张宇豪;吴迅冬;唐华锦;

参考文献(References)：

• [1]HODGKIN A L,HUXLEY A F,et al.A quantitative description of membrane current and its application to conduction and excitation in nerve[J].The Journal of Physiology,1952,117,4:500-544.
• [2]SCHMID G,GOYCHUK I,H?NGGI P.Effect of channel block on the spiking activity of excitable membranes in a stochastic Hodgkin-Huxley model[J].Physical Biology,2004,1(2):61.DOI:10.1088/1478-3967/1/2/002.
• [3]ABBOTT L F.Lapicque’s introduction of the integrate-and-fre model neuron[J].Brain Research Bulletin,1999,50(5-6):303-304.DOI:10.1016/s0361-9230(99)00161-6.
• [4]BRETTE R,GERSTNER W.Adaptive Exponential Integrate-and-Fire Model as an Effective Description of Neuronal Activity[J].Journal of Neurophysiology,2005,94(5):3637-3642.DOI:10.1152/jn.00686.2005.
• [5]IZHIKEVICH E M.Simple model of spiking neurons[J].IEEE Transactions on Neural Networks,2003,14(6):1569-1572.DOI:10.1109/TNN.2003.820440.
• [6]STEPHAN K E,BALDEWEG T,FRISTON K J,et al.Synaptic Plasticity and Dysconnection in Schizophrenia[J].Biological Psychiatry,2006,59(10):929-939.DOI:10.1016/j.biopsych.2005.10.005.
• [7]BARAK O.Recurrent neural networks as versatile tools of neuroscience research[J].Current Opinion in Neurobiology,2017,46:1-6.DOI:10.1016/j.conb.2017.06.003.
• [8]COOMBES S.Waves,bumps,and patterns in neural feld theories[J].Biological Cybernetics,2005,93(2):91-108.DOI:10.1007/s00422-005-0574-y.
• [9]GLEESON P,STEUBER V,SILVER R A.Neuroconstruct:a tool for modeling networks of neurons in 3d space[J].Neuron,2007,54(2):219-235.DOI:10.1016/j.neuron.2007.03.025.
• [10]GEWALTIG M O,DIESMANN M.Nest (neural simulation tool)[J].Scholarpedia,2007,2(4):1430.DOI:10.4249/scholarpedia.1430.
• [11]GOODMAN D F M,BRETTE R.The Brian Simulator[J].Frontiers in Neuroscience,2009,3(2).DOI:10.3389/neuro.01.026.2009.
• [12]WANG C,CHEN X,ZHANG T,et al.Brain Py:a flexible,integrative,efficient,and extensible framework towards generalpurpose brain dynamics programming[J].Neuroscience (preprint).DOI:10.1101/2022.10.28.514024.
• [13]CARNEVALE N T,HINES M L.The NEURON Book[M].Cambridge:Cambridge University Press,2006.DOI:10.1017/CBO9780511541612.
• [14]WILSON M,BHALLA U,UHLEY J,et al.GENESIS:A System for Simulating Neural Networks[J].Advances in Neural Information Processing Systems,1988,1:485-492.
• [15]CHOU T S,KASHYAP H J,XING J W,et al.CARLsim 4:an open source library for large scale,biologically detailed spiking neural network simulation using heterogeneous clusters[C]//2018 International Joint Conference on Neural Networks (IJCNN).IEEE,2018:1-8.DOI:10.1109/IJCNN.2018.8489326.
• [16]NIEDERMEIER L,CHEN K S,XING J W,et al.CARLsim 6:An Open Source Library for Large-Scale,Biologically Detailed Spiking Neural Network Simulation[C]//2022 International Joint Conference on Neural Networks (IJCNN).IEEE,2022:1-10.DOI:10.1109/IJCNN55064.2022.9892644.
• [17]智源研究院.智源天演人工智能生命模拟工程-高精度生命智能模拟启发通用AI.[2022-12-04].https://www.baai.ac.cn/portal/list/index/id/45.html.
• [18]BEKOLAY T,BERGSTRA J,HUNSBERGER E,et al.Nengo:a Python tool for building large-scale functional brain models[J].Frontiers in Neuroinformatics,2014,7.DOI:10.3389/fninf.2013.00048.
• [19]HAZAN H,SAUNDERS D J,KHAN H,et al.Binds NET:A Machine Learning-Oriented Spiking Neural Networks Library in Python[J].Frontiers in Neuroinformatics,2018,12.DOI:10.3389/fninf.2018.00089.
• [20]fangwei123456.Spiking Jelly[EB/OL].(2022-12-02)[2022-12-04].https://github.com/fangwei123456/spikingjelly.
• [21]HONG C F,YUAN M W,ZHANG M X,et al.SPAIC:A Spike-based Artifcial Intelligence Computing Framework[EB/OL].(2022-07-26)[2022-12-04].http://arxiv.org/abs/2207.12750.
• [22]ZENG YI,ZHAO D C,ZHAO F F,et al.Brain Cog:A Spiking Neural Network based Brain-inspired Cognitive Intelligence Engine for Brain-inspired AI and Brain Simulation.(2022-07-18)[2022-12-04].http://arxiv.org/abs/2207.08533.
• [23]中国传媒大学脑科学与智能媒体研究院.Ni Mi Brain Cloud脑仿真云计算平台.[2022-11-17].http://nimibrain.cuc.edu.cn/.
• [24]CHEN Y,XIE Y,SONG L,et al.A Survey of Accelerator Architectures for Deep Neural Networks[J].Engineering,2020,6(3):264-274.DOI:10.1016/j.eng.2020.01.007.
• [25]KNIGHT J C,NOWOTNY T.Larger GPU-accelerated brain simulations with procedural connectivity[J].Nature Computational Science,2021,1(2).DOI:10.1038/s43588-020-00022-7.
• [26]VAN DER VLAG M A,SMARAGDOS G,AL-ARS Z,et al.Exploring complex brain-simulation workloads on multi-GPUdeployments[J].ACM Transactions on Architecture and Code Optimization,2019,16(4):1-25.DOI:10.1145/3371235.
• [27]ELIASMITH C,STEWART T,CHOO X,et al.A Large-Scale Model of the Functioning Brain[J].Science,2012,338(6111):1202-1205.DOI:10.1126/science.1225266.
• [28]SCHEMMEL J,BRüDERLE D,GRüBL A,et al.A wafer-scale neuromorphic hardware system for large-scale neural modeling[C]//2010 IEEE International Symposium on Circuits and Systems (ISCAS).IEEE,2010:1947-1950.DOI:10.1109/ISCAS.2010.5536970.
• [29]STRADMANN Y,BILLAUDELLE S,BREITWIESER O,et al.Demonstrating analog inference on the Brain Scale S-2 mobile system[J].IEEE Open Journal of Circuits and Systems,2022,3:252-262.DOI:10.1109/OJCAS.2022.3208413.
• [30]BENJAMIN B V,GAO P,MCQUINN E,et al.Neurogrid:a mixed-analog-digital multichip system for large-scale neural simulations[J].Proceedings of the IEEE,2014,102(5):699-716.DOI:10.1109/JPROC.2014.2313565.
• [31]FURBER S B,GALLUPPI F,TEMPLE S,et al.The Spi NNaker project[J].Proceedings of the IEEE,2014,102(5):652-665.DOI:10.1109/JPROC.2014.2304638.
• [32]MUNDY A.Real time spaun on Spi NNaker-functional brain simulation on a massively-parallel computer architecture[D].Manchester:The University of Manchester,2017.
• [33]MEROLLA P A,ARTHUR J,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.DOI:10.1126/science.1254642.
• [34]DAVIES M,SRINIVASA N,LIN T H,et al.Loihi:a neuromorphic manycore processor with on-chip learning[J].IEEE Micro,2018,38(1):82-99.DOI:10.1109/MM.2018.112130359.
• [35]Intel.Intel Advances Neuromorphic with Loihi 2,New Lava Software Framework.(2021-09-30)[2022-12-05].https://www.intel.com/content/www/us/en/newsroom/news/intel-unveils-neuromorphic-loihi-2-lava-software.html.
• [36]Intel.Intel Scales Neuromorphic Research System to 100 Million Neurons[EB/OL].(2020-03-18)[2022-10-05].https://newsroom.intel.com/news/intel-scales-neuromorphic-research-system-100-million-neurons/.
• [37]Intel.Intel’s Pohoiki Beach,a 64-Chip Neuromorphic System,Delivers Breakthrough Results in Research Tests[EB/OL].(2019-07-19)[2022-12-05].https://newsroom.intel.com/news/intels-pohoiki-beach-64-chip-neuromorphic-system-delivers-breakthroughresults-research-tests/.
• [38]PEI J,DENG L,SONG S,et al.Towards artifcial general intelligence with hybrid Tianjic chip architecture[J].Nature,2019,572(7767):106-111.DOI:10.1038/s41586-019-1424-8.
• [39]之江实验室.达尔文类脑硬件.[2022-12-05].https://www.darwinware.com/hardware/.
• [40]IZHIKEVICH E M,EDELMAN G M.Large-scale model of mammalian thalamocortical systems[J].Proceedings of the National Academy of Sciences of the United States of America,2008,105(9):3593-3598.DOI:10.1073/pnas.0712231105.
• [41]ANANTHANARAYANAN R,ESSER S K,SIMON H D,et al.The cat is out of the bag:cortical simulations with 10 9 neurons,1013 synapses[C]//Proceedings of the Conference on High Performance Computing Networking,Storage and Analysis-SC’09,Portland,Oregon.New York:Association for Computing Machinery,2009:1-12.DOI:10.1145/1654059.1654124.
• [42]MARKRAM H,MULLER E,RAMASWAMY S,et al.Reconstruction and Simulation of Neocortical Microcircuitry[J].Cell,2015,163(2):456-492.DOI:10.1016/j.cell.2015.09.029.
• [43]REIMANN M W,BOLA?OS-PUCHET S,COURCOL J,et al.Modeling and Simulation of Rat Non-Barrel Somatosensory Cortex.Part I:Modeling Anatomy[EB/OL].(2022-08-15)[2022-12-05].DOI:10.1101/2022.08.11.503144.
• [44]POTJANS T C,DIESMANN M.The Cell-Type Specifc Cortical Microcircuit:Relating Structure and Activity in a Full-Scale Spiking Network Model[J].Cerebral cortex,2014,24(3):785-806.DOI:10.1093/cercor/bhs358.
• [45]CHARIKER L,SHAPLEY R,YOUNG L S.Orientation Selectivity from Very Sparse LGN Inputs in a Comprehensive Model of Macaque V1 Cortex[J].Journal of Neuroscience,2016,36(49):12368-12384.DOI:10.1523/JNEUROSCI.2603-2616.
• [46]MERKT B,SCHü?LER F,ROTTER S.Propagation of orientation selectivity in a spiking network model of layered primary visual cortex[J].PLo S Computational Biology,2019,15(7):e1007080.DOI:10.1371/journal.pcbi.1007080.
• [47]ANTOLIK J,BEDNAR J.Development of Maps of Simple and Complex Cells in the Primary Visual Cortex[J].Frontiers in Computational Neuroscience,2011,5.DOI:10.3389/fncom.2011.00017.
• [48]LARISCH R,G?NNER L,TEICHMANN M,et al.Sensory coding and contrast invariance emerge from the control of plastic inhibition over emergent selectivity[J].PLo S Computational Biology,2021,17(11):e1009566.DOI:10.1371/journal.pcbi.1009566.
• [49]TOMKOVáM,TOMEK J,NOVáK O,et al.Formation and disruption of tonotopy in a large-scale model of the auditory cortex[J].Journal of Computational Neuroscience,2015,39(2):131-153.DOI:10.1007/s10827-015-0568-2.
• [50]KUDELA P,BOATMAN-REICH D,BEEMAN D,et al.Modeling Neural Adaptation in Auditory Cortex[J].Frontiers in Neural Circuits,2018,12.DOI:10.3389/fncir.2018.00072.
• [51]BURAK Y,FIETE I R.Accurate Path Integration in Continuous Attractor Network Models of Grid Cells[J].PLo SComputational Biology,2009,5(2):e1000291.DOI:10.1371/journal.pcbi.1000291.
• [52]TANG H,HUANG W,NARAYANAMOORTHY A,et al.Cognitive memory and mapping in a brain-like system for robotic navigation[J].Neural Networks,2017,87:27-37.DOI:10.1016/j.neunet.2016.08.015.
• [53]MATTAR M G,DAW N D.Prioritized memory access explains planning and hippocampal replay[J].Nature Neuroscience,2018,21(11).DOI:10.1038/s41593-018-0232-z.
• [54]WEI Y,KRISHNAN G P,KOMAROV M,et al.Differential roles of sleep spindles and sleep slow oscillations in memory consolidation[J].PLo S Computational Biology,2018,14(7):e1006322.DOI:10.1371/journal.pcbi.1006322.
• [55]POO M.Whereto the mega brain projects?[J].National Science Review,2014,1(1):12-14.DOI:10.1093/nsr/nwt019.
• [56]ANTICEVIC A,MURRAY J D,BARCH D M,et al.Bridging Levels of Understanding in Schizophrenia Through Computational Modeling[J].Clinical Psychological Science,2015,3(3):433-459.DOI:10.1177/2167702614562041.
• [57]DURSTEWITZ D,SEAMANS J K.The Dual-State Theory of Prefrontal Cortex Dopamine Function with Relevance to CatecholO-Methyltransferase Genotypes and Schizophrenia[J].Biological Psychiatry,2008,64(9):739-749.DOI:10.1016/j.biopsych.2008.05.015.
• [58]MURRAY J D,DEMIRTA?M,ANTICEVIC A.Biophysical Modeling of Large-Scale Brain Dynamics and Applications for Computational Psychiatry[J].Biological Psychiatry:Cognitive Neuroscience and Neuroimaging,2018,3(9):777-787.DOI:10.1016/j.bpsc.2018.07.004.
• [59]WANG X J,KRYSTAL J H.Computational Psychiatry[J].Neuron,2014,84(3):638-654.DOI:10.1016/j.neuron.2014.10.018.
• [60]MURRAY J D,WANG X J.Chapter 1-Cortical Circuit Models in Psychiatry:Linking Disrupted Excitation-Inhibition Balance to Cognitive Defcits Associated With Schizophrenia[M]//Computational Psychiatry.Academic Press,2018:3-25.DOI:10.1016/B978-0-12-809825-7.00001-8.
• [61]LU M,GUO Z,GAO Z,et al.Multiscale Brain Network Models and Their Applications in Neuropsychiatric Diseases[J].Electronics,2022,11(21).DOI:10.3390/electronics11213468.
• [62]VAN NIFTERICK A M,GOUW A A,VAN KESTEREN R E,et al.A multiscale brain network model links Alzheimer’s diseasemediated neuronal hyperactivity to large-scale oscillatory slowing[J].Alzheimer's Research&Therapy,2022,14(1):101.DOI:10.1186/s13195-022-01041-4.
• [63]ZIMMERMANN J,PERRY A,BREAKSPEAR M,et al.Differentiation of Alzheimer’s disease based on local and global parameters in personalized Virtual Brain models[J].Neuro Image:Clinical,2018,19:240-251.DOI:10.1016/j.nicl.2018.04.017.
• [64]WEI Y,ULLAH G,INGRAM J,et al.Oxygen and seizure dynamics:II.Computational modeling[J].Journal of Neurophysiology,2014,112(2):213-223.DOI:10.1152/jn.00541.2013.
• [65]TANG J,ZHANG J,MA J,et al.Astrocyte calcium wave induces seizure-like behavior in neuron network[J].Science China Technological Sciences,2017,60(7):1011-1018.DOI:10.1007/s11431-016-0293-9.
• [66]LEWIS D A,HASHIMOTO T,VOLK D W.Cortical inhibitory neurons and schizophrenia[J].Nature Reviews Neuroscience,2005,6(4).DOI:10.1038/nrn1648.
• [67]NAKAZAWA K,ZSIROS V,JIANG Z,et al.GABAergic interneuron origin of schizophrenia pathophysiology[J].Neuropharmacology,2012,62(3):1574-1583.DOI:10.1016/j.neuropharm.2011.01.022.
• [68]UHLHAAS P J.Dysconnectivity,large-scale networks and neuronal dynamics in schizophrenia[J]Current Opinion in Neurobiology,2013,23(2):283-290.DOI:10.1016/j.conb.2012.11.004.
• [69]CABRAL J,FERNANDES H M,VAN HARTEVELT T J,et al.Structural connectivity in schizophrenia and its impact on the dynamics of spontaneous functional networks[J].Chaos (Woodbury,N.Y.),2013,23(4):046111.DOI:10.1063/1.4851117.
• [70]YANG G,MURRAY J,WANG X J,et al.Functional hierarchy underlies preferential connectivity disturbances in schizophrenia[J].Proceedings of the National Academy of Sciences,2016,113(2):E219-E228.DOI:10.1073/pnas.1508436113.
• [71]TERMAN D,RUBIN J E,YEW A C,et al.Activity Patterns in a Model for the Subthalamopallidal Network of the Basal Ganglia[J].Journal of Neuroscience,2002,22(7):2963-2976.DOI:10.1523/JNEUROSCI.22-07-02963.2002.
• [72]PIRINI M,ROCCHI L,SENSI M,et al.A computational modelling approach to investigate different targets in deep brain stimulation for Parkinson’s disease[J].Journal of Computational Neuroscience,2002,26(1):91.DOI:10.1007/s10827-008-0100-z.
• [73]SCHIFF S J.Towards model-based control of Parkinson’s disease[J].Philosophical Transactions of the Royal Society,2010,368(1918):2269-2308.DOI:10.1098/rsta.2010.0050.
• [74]LIU C,ZHOU C,WANG J,et al.Delayed Feedback-Based Suppression of Pathological Oscillations in a Neural Mass Model[J].IEEE Transactions on Cybernetics,2021,51(10):5046-5056.DOI:10.1109/TCYB.2019.2923317.
• [75]LECUN Y,BENGIO Y.Convolutional networks for images,speech,and time series[M]//The Handbook of Brain Theory and Neural Networks.Cambridge:MIT Press,1998:255-258.
• [76]SCHULTZ W,DAYAN P,MONTAGUE P R.A Neural Substrate of Prediction and Reward[J].Science,275(5306):1593-1599.DOI:10.1126/science.275.5306.1593.
• [77]HASANI R,LECHNER M,AMINI A,et al.Closed-form continuous-time neural networks[J].Nature Machine Intelligence,2022,4(11).DOI:10.1038/s42256-022-00556-7.
• [78]KUDITHIPUDI D,AGUILAR-SIMON M,BABB J,et al.Biological underpinnings for lifelong learning machines[J].Nature Machine Intelligence,2022,4(3).DOI:10.1038/s42256-022-00452-0.
• [79]WU X,LIU X,LI W,et al.Improved expressivity through dendritic neural networks[J].Advances in neural information processing systems,2018,31.
• [80]BARTUNOV S,SANTORO A,RICHARDS B,et al.Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures[J].Advances in Neural Information Processing Systems,2018,31.
• [81]BENGIO Y,LEE D H,BORNSCHEIN J,et al.Towards Biologically Plausible Deep Learning[EB/OL].(2016-08-08)[2022-12-05].https://arxiv.org/abs/1502.04156.
• [82]Xiao W,Chen H,Liao Q,et al.Biologically-Plausible Learning Algorithms Can Scale to Large Datasets[C]//International Conference on Learning Representations 2019.ICRL,2019.