针刺神经电信号编码与解码的研究
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摘要
神经系统以时间空间编码的形式表征外部刺激所包含的信息,但神经系统对于针刺等外部刺激编码解码以及神经电信号传输通路建模的研究鲜见报道。本论文一方面根据针刺传输通路的生理结构建立针刺前馈网络信号传输模型并研究其动力学特性,另一方面基于实验数据从编码、解码、复杂网络等角度挖掘针刺神经信号,并与模型分析的结果对照。
     为揭示针刺作用机理,本文根据针刺神经信息传输通路的生理结构建立了针刺信号传输通路的前馈网络模型。根据进一步假设把针刺作用等效为噪声,发现针刺对调节网络编码特性(放电率和放电规则性)具有重要作用。噪声下前馈网络发生了相干共振现象,表明一定强度的针刺可能引起网络共振和同步,从而改变神经元网络的特性,进而产生针刺效应。把针刺刺激等效成伪周期信号,发现耦合神经元网络的相干共振能够由异质性非周期信号诱导产生,通过改变异质性非周期信号的平均幅值、周期或者异质性可以改变网络相干共振的特性。模型研究表明网络共振和同步是针刺发生作用的可能机理。
     在针刺信号通路模型分析的基础上,本文设计了不同手法和频率的针刺作用大鼠足三里的动物实验,获取了脊髓背根神经电信号,根据放电类选算法提取针刺神经信号编码特征,首次发现不同手法所对应的时空编码形式存在本质的差别,尤其是捻转法与提插法的编码形式差异较大;这种差异性体现在神经元对特定针刺手法的选择性,但对同一手法不同频率的针刺作用,这种编码选择性并不明显;还发现了神经系统对于针刺作用具有适应性和饱和特性。
     在此基础上,利用贝叶斯解码算法通过编码分析的结果反向预测手法,预测成功率接近百分之九十。通过统计分析,证明所得结果具有统计意义,并使用互信息量化了针刺信息。这些结果可以直接应用于神经系统和机器接口的实现。本文首次提出采用复杂网络映射的方法分析针刺神经电信号,证明捻转法和提插法两种针刺手法的内在特征是不同的。
     本文对实验数据的特征和针刺前馈网络模型的特征进行对比,发现针刺前馈网络可以复现针刺现象,证明了前馈网络模型的合理性。实验和模型研究的结合对进一步理解针刺和提高临床治疗的效率具有非凡的意义。
Neural system characterizes information of external stimulation by spa-tiotemporal encoding. However, there are rarely reports about the encodingform of acupuncture stimulation and the model of electrical signal transmissionpath. We try to explore the underlying mechanism of acupuncture by construct-ing and analyzing the feedforward neuronal model and mining the experimentalsignals based on encoding, decoding algorithm and complex network mappingmethod.
     First, we constructed a feedforward network (FFN) of FitzHugh-Nagumo(FHN)neurons according to the feedforward pathway of acupuncture signal. Here, theacupuncture stimulation is equivalent to noise. It is found that acupuncture playsan important role in modulating the transmission of fring rate and spiking reg-ularity. Furthermore, noise could induce coherent resonance in the feedforwardnetwork. Therefore, acupuncture with certain intensity can induced resonance inneuronal network, which would afect the activities of the neural system. More-over, after assuming the acupuncture stimuli to be aperiodic signal, it is foundthat resonance can also be induced by heterogenous aperiodic signals, and pa-rameters of the aperiodic signal can modify the resonance of the coupled network.These studies imply that resonance and synchronization may be the mechanismof acupuncture.
     To further understand the mechanism of acupuncture, experiments are de-signed that manual acupuncture (MA) manipulations with diferent types andfrequencies are taken at 'Zusanli' points of experiment rats. We found the spa-tiotemporal coding pattern of diferent acupuncture manipulations. The difer-ences between features of manipulation 'nb''nx' and those of manipulation 'tb''tx' are obvious. However, neuronal selectivity of encoding is not obvious whenthe same manipulation is taken with diferent frequencies. Neuronal adaptivityand saturation phenomenon are also observed when acupuncture with diferentfrequencies are taken.
     Types of acupuncture manipulations taken on the rats are inferred witha high probability (about90%) by Bayesian decoding algorithm based on theresponse of multiple neurons. These results are proved to be signifcant by s-tatistical analysis. Furthermore, mutual information is applied to quantify thedecoding process. These studies may help to construct the interface betweenneural systems and machines and improve the clinical study. We also proposecomplex network mapping method to analyze the signal. It indicates that theinherent characteristics of MA 'nb''nx' and those of MA 'tb''tx' are diferent.
     After compare the experimental results and the model analysis, we foundthat some key properties in experiment can be reproduced by the feedforwardmodel. This fnding imply that the modelling strategy is reasonable. The combi-nation of experimental and computational study is helpful to improve the clinicaltreatment of acupuncture.
引文
[1] S. Andersson,T. Lundeberg, Acupuncture–from empiricism to science: func-tional background to acupuncture efects in pain and disease, Med Hypothe-ses,1995,45(3):271-281.
    [2] K. VanderPloeg,X. Yi, Acupuncture in Modern Society, Journal of Acupunc-ture and Meridian Studies,2009,2(1):26-33.
    [3] R. Leake,J. E. Broderick, Treatment Efcacy Of Acupuncture: A ReviewOf the Research Literature, Integrative Medicine,1999,1(3):107-115.
    [4] P. H. Richardson,C. A. Vincent, Acupuncture for the treatment of pain: areview of evaluative research, Pain,1986,24(1):15-40.
    [5] J. Ezzo, B. Berman, V. A. Hadhazy, et al., Is acupuncture efective for thetreatment of chronic pain? A systematic review, Pain,2000,86(3):217-225.
    [6] J. M. Foster,B. P. Sweeney, The mechanisms of acupuncture analgesia, BrJ Hosp Med,1987,38(4):308-312.
    [7] D. A. Tang, Advances in research on the mechanism of acupuncture andmoxibustion, Zhen Ci Yan Jiu,1987,12(4):278-284.
    [8] Z. H. Cho, S. C. Chung, J. P. Jones, et al., New fndings of the correlationbetween acupoints and corresponding brain cortices using functional MRI,Proc Natl Acad Sci U S A,1998,95(5):2670-2673.
    [9] Y. Zhang, J. Liang, W. Qin, et al., Comparison of visual cortical activationsinduced by electro-acupuncture at vision and nonvision-related acupoints,Neurosci. Lett.,2009,458(1):6-10.
    [10] W. T. Zhang, Z. Jin, G. H. Cui, et al., Relations between brain networkactivation and analgesic efect induced by low vs. high frequency electricalacupoint stimulation in diferent subjects: a functional magnetic resonanceimaging study, Brain Res,2003,982(2):168-178.
    [11] J. S. Han, Acupuncture: neuropeptide release produced by electrical stimu-lation of diferent frequencies, Trends Neurosci,2003,26(1):17-22.
    [12] T. T. H. Wang, Y. Yuan, Y. Kang, et al., Efects of acupuncture on theexpression of glial cell line-derived neurotrophic factor (GDNF) and basicfbroblast growth factor (FGF-2/bFGF) in the left sixth lumbar dorsal rootganglion following removal of adjacent dorsal root ganglia, Neurosci Lett,2005,382(3):236-241.
    [13] J. O’Connor, D. Bensky, Acupuncture. Shanghai College of TraditionalMedicine, a comprehensive text, InEastland Press, Seattle1981.
    [14] X.N. Cheng, Chinese Acupuncture and Moxibustion, Foreign LanguagesPress1987.
    [15] M. Backer, M. G. Hammes, M. Valet, et al., Diferent modes of manualacupuncture stimulation diferentially modulate cerebral blood fow velocity,arterial blood pressure and heart rate in human subjects, Neurosci Lett,2002,333(3):203-206.
    [16] T. Friedemann,W. M. Li,Z. J. Wang, Inhibitory regulation of blood pressureby manual acupuncture in the anesthetized rat, Autonomic Neuroscience-Basic&Clinical,2009,151(2):178-182.
    [17] M. Rabinovich, P. Varona, A. Selverston, et al., Dynamical principles inneuroscience, Rev Mod Phys,2006,78(4):1213-1265.
    [18] F. Rieke, D. Warland, R. de Ruyter van Steveninck, W. Bialek, Spikes:Exploring the Neural Code. The MIT Press, Cambridge, MA1997.
    [19] P. Dayan and L.F. Abbott, Theoretical Neuroscience Computational andMathematical Modeling of Neural Systems, MIT Press, Cambridge,2001.
    [20] E.R. Kandel, J.H. Schwartz, T.M. Jssell, Principles of Neural Science. Mc-Graw Hill, New York2000.
    [21] R. Quian Quiroga, S. Panzeri, Extracting information from neuronal pop-ulations: information theory and decoding approaches, Nat Rev Neurosci,2009,10(3):173-185.
    [22] R. Q. Quiroga, Z. Nadasdy, Y. Ben-Shaul, Unsupervised spike detection andsorting with wavelets and superparamagnetic clustering, Neural Computing,2004,16(8):1661-1687.
    [23] L. F. Abbott, Decoding neuronal fring and modelling neural networks,Quart Rev Biophys,1994,27(3):291-331.
    [24] M. W. Oram, P. Foldiak, D. I. Perrett, et al., The’Ideal Homunculus’:decoding neural population signals, Trends Neurosci,1998,21(6):259-265.
    [25] R. Q. Quiroga, L. Reddy, C. Koch, et al., Decoding visual inputs frommultiple neurons in the human temporal lobe, J Neurophysiol,2007,98(4):1997-2007.
    [26] R. Quian Quiroga, L. H. Snyder, A. P. Batista, et al., Movement intention isbetter predicted than attention in the posterior parietal cortex, J Neurosci,2006,26(13):3615-3620.
    [27] R. Q. Quiroga, L. Reddy, G. Kreiman, et al., Invariant visual representationby single neurons in the human brain, Nature,2005,435(7045):1102-1107.
    [28] L. Cozzi, P. D’Angelo, M. Chiappalone, et al., Coding and decoding ofinformation in a bi-directional neural interface, Neurocomputing,2005,65-66:783-792.
    [29] C. Takeshige, K. Oka, T. Mizuno, et al., The acupuncture point and itsconnecting central pathway for producing acupuncture analgesia, Brain ResBull,1993,30(1-2):53-67.
    [30] S. A. Andersson,E. Holmgren, On acupuncture analgesia and the mechanismof pain, Am J Chin Med (Gard City N Y),1975,3(4):311-334.
    [31] G. Li,E. S. Yang, An fMRI study of acupuncture-induced brain activation ofaphasia stroke patients, Complement Ther Med,2011,19Suppl1: S49-59.
    [32] F. Politti, C. F. Amorim, L. Calili, et al., The use of surface electromyo-graphy for the study of auricular acupuncture, J Bodyw Mov Ther,2010,14(3):219-226.
    [33] K. Spaulding,K. Chamberlin, The transport of extremely low-frequency elec-trical signals through an acupuncture meridian compared to nonmeridiantissue, J Altern Complement Med,2011,17(2):127-132.
    [34] D. D. Dougherty, J. Kong, M. Webb, et al., A combined [11C]diprenorphinePET study and fMRI study of acupuncture analgesia, Behav Brain Res,2008,193(1):63-68.
    [35] J. Wang, L. Sun, X. Fei, et al., Chaos analysis of the electrical signal timeseries evoked by acupuncture, Chaos Solitons&Fractals,2007,33(3):901-907.
    [36] W. J. Han Chun-Xiao, Che Yan-Qiu,Deng Bin,Guo Yi,Guo Yong-Ming andLiu Yang-Yang, F, AcPSn,2010,59(8):5880-5887.
    [37] G. Baier,R. S. Leder,P. Parmananda, Human Electroencephalogram InducesTransient Coherence in Excitable Spatiotemporal Chaos, Phys Rev Lett,2000,84(19):4501-4504.
    [38] E. D. Adrian, The impulses produced by sensory nerve endings, The Journalof Physiology,1926,61(1):49-72.
    [39] R. R. de Ruyter van Steveninck, G. D. Lewen, S. P. Strong, et al., Repro-ducibility and variability in neural spike trains, Science,1997,275(5307):1805-1808.
    [40] R. C. deCharms,M. M. Merzenich, Primary cortical representation of soundsby the coordination of action-potential timing, Nature,1996,381(6583):610-613.
    [41] Z. Mainen,T. Sejnowski, Reliability of spike timing in neocortical neurons,Science,1995,268(5216):1503-1506.
    [42] J. W. McClurkin, L. M. Optican, B. J. Richmond, et al., Concurrent pro-cessing and complexity of temporally encoded neuronal messages in visualperception, Science,1991,253(5020):675-677.
    [43] M. R. Mehta,A. K. Lee,M. A. Wilson, Role of experience and oscillationsin transforming a rate code into a temporal code, Nature,2002,417(6890):741-746.
    [44] P. Reinagel,R. C. Reid, Precise fring events are conserved across neurons,J Neurosci,2002,22(16):6837-6841.
    [45] J. P. Segundo, G. Sugihara, P. Dixon, et al., The spike trains of inhibitedpacemaker neurons seen through the magnifying glass of nonlinear analyses,Neuroscience,1998,87(4):741-766.
    [46] M. N. Shadlen,W. T. Newsome, The variable discharge of cortical neuron-s: Implications for connectivity, computation, and information coding, J.Neurosci.,1998,18(10):3870-3896.
    [47] W. R. Softky, Simple codes versus efcient codes, Curr Opin Neurobiol,1995,5(2):239-247.
    [48] A. Kumar,S. Rotter,A. Aertsen, Spiking activity propagation in neuronalnetworks: reconciling diferent perspectives on neural coding, Nat Rev Neu-rosci,2010,11(9):615-627.
    [49] A. Kumar,S. Rotter,A. Aertsen, Conditions for propagating synchronousspiking and asynchronous fring rates in a cortical network model, J Neurosci,2008,28(20):5268-5280.
    [50] M. C. W. van Rossum,G. G. Turrigiano,S. B. Nelson, Fast propagation offring rates through layered networks of noisy neurons, J Neurosci,2002,22(5):1956-1966.
    [51] M. Diesmann,M. O. Gewaltig,A. Aertsen, Stable propagation of synchronousspiking in cortical neural networks, Nature,1999,402(6761):529-533.
    [52] M. Li, H. Greenside, Stable propagation of a burst through a one-dimensional homogeneous excitatory chain model of songbird nucleus HVC,Phys Rev E,2006,74(1):011918.
    [53] I. Segev, Synchrony is stubborn in feedforward cortical networks, Nat Neu-rosci,2003,6(6):543-544.
    [54] S. T. Wang,W. Wang,F. Liu, Propagation of fring rate in a feed-forwardneuronal network, Phys Rev Lett,2006,96(1):018103.
    [55] M. Yi,L. J. Yang, Propagation of fring rate by synchronization and coher-ence of fring pattern in a feed-forward multilayer neural network, Phys RevE,2010,81(6):061924.
    [56] B. Lindner, J. Garca-Ojalvo, A. Neiman, et al., Efects of noise in excitablesystems, Physics Reports-Review Section of Physics Letters,2004,392(6):321-424.
    [57] A. S. Pikovsky,J. Kurths, Coherence Resonance in a Noise-Driven ExcitableSystem, Phys Rev Lett,1997,78(5):775-778.
    [58] C. M. Gray, P. Konig, A. K. Engel, et al., Oscillatory responses in cat visualcortex exhibit inter-columnar synchronization which refects global stimulusproperties, Nature,1989,338(6213):334-337.
    [59] T. Womelsdorf, P. Fries, P. P. Mitra, et al., Gamma-band synchronizationin visual cortex predicts speed of change detection, Nature,2006,439(7077):733-736.
    [60] E. Manjarrez, J. G. Rojas-Piloni, I. Mendez, et al., Internal stochastic res-onance in the coherence between spinal and cortical neuronal ensembles inthe cat, Neurosci Lett,2002,326(2):93-96.
    [61] T. Kreuz,S. Luccioli,A. Torcini, Double Coherence Resonance in NeuronModels Driven by Discrete Correlated Noise, Phys Rev Lett,2006,97(23):238101.
    [62] C. Zhou,J. Kurths,B. Hu, Array-Enhanced Coherence Resonance: NontrivialEfects of Heterogeneity and Spatial Independence of Noise, Phys Rev Lett,2001,87(9):098101.
    [63] J. F. Lindner, B. K. Meadows, W. L. Ditto, et al., Array Enhanced Stochas-tic Resonance and Spatiotemporal Synchronization, Phys Rev Lett,1995,75(1):3-6.
    [64] P. Jung, G. Mayer-Kress, Spatiotemporal Stochastic Resonance in ExcitableMedia, Phys Rev Lett,1995,74(11):2130-2133.
    [65] Q. S. Li, X. F. Lang, Internal signal transmission in one-way coupled ex-citable system: Noise and coupling efects, Phys Rev E,2006,74(3):031905.
    [66] B. Hu, C. Zhou, Phase synchronization in coupled nonidentical excitablesystems and array-enhanced coherence resonance, Phys Rev E Stat PhysPlasmas Fluids Relat Interdiscip Topics,2000,61(2): R1001-R1004.
    [67] Y. Q. Wang, D. T. W. Chik, Z. D. Wang, Coherence resonance and noise-induced synchronization in globally coupled Hodgkin-Huxley neurons, PhysRev E,2000,61(1):740-746.
    [68] M. Gosak, D. Korosak, M. Marhl, Optimal network confguration for max-imal coherence resonance in excitable systems, Phys Rev E,2010,81(5):056104.
    [69] D. Q. Guo, C. G. Li, Stochastic and coherence resonance in feed-forward-loop neuronal network motifs, Phys Rev E,2009,79(5):051921.
    [70] M. Ozer, M. Perc, M. Uzuntarla, Controlling the spontaneous spiking regu-larity via channel blocking on Newman-Watts networks of Hodgkin-Huxleyneurons, Epl,2009,86(4):40008.
    [71] X. M. Li, J. Zhang, M. Small, Self-organization of a neural network withheterogeneous neurons enhances coherence and stochastic resonance, Chaos,2009,19(1):013126.
    [72] S. Sinha, Noise-free stochastic resonance in simple chaotic systems, PhysicaA: Statistical Mechanics and its Applications,1999,270(1-2):204-214.
    [73] P.S. Landa, P.V.E. McClintock, Vibrational resonance, Journal of Physicsa-Mathematical and General,2000,33(45): L433-L438.
    [74] E. Ullner, A. Zaikin, J. Garcia-Ojalvo, R. Bascones, J. Kurths, Vibrationalresonance and vibrational propagation in excitable systems, PHYSICS LET-TERS A,2003,312(5-6):348-354.
    [75] A.A. Zaikin, L. Lopez, J.P. Baltanas, J. Kurths, M.A.F. Sanjuan, Vibra-tional resonance in a noise-induced structure, Phys Rev E,2002,66(1):011106.
    [76] J.H. Yang, X.B. Liu, Controlling vibrational resonance in a multistable sys-tem by time delay, Chaos: An Interdisciplinary Journal of Nonlinear Science,2010,20(3):033124.
    [77] S. Luccioli, T. Kreuz, A. Torcini, Dynamical response of the Hodgkin-Huxleymodel in the high-input regime, Phys Rev E,2006,73(4):041902.
    [78] H. Wang, L. Wang, L. Yu, Y. Chen, Response of Morris-Lecar neurons tovarious stimuli, Phys Rev E,2011,83(2):021915.
    [79] K.H. Pettersen, G.T. Einevoll, Amplitude variability and extracellular low-pass fltering of neuronal spikes, Biophys J,2008,94(3):784-802.
    [80] A. D. Reyes, Synchrony-dependent propagation of fring rate in iterativelyconstructed networks in vitro, Nat Neurosci,2003,6(6):593-599.
    [81] C. Martin, R. Gervais, E. Hugues, et al., Learning modulation of odor-induced oscillatory responses in the rat olfactory bulb: a correlate of odorrecognition?, J Neurosci,2004,24(2):389-397.
    [82] P. J. Uhlhaas, W. Singer, Neural synchrony in brain disorders: relevance forcognitive dysfunctions and pathophysiology, Neuron,2006,52(1):155-168.
    [83] W. Singer, Consciousness and the binding problem, Ann N Y Acad Sci,2001,929:123-146.
    [84] A. K. Engel, W. Singer, Temporal binding and the neural correlates of sen-sory awareness, Trends Cogn Sci,2001,5(1):16-25.
    [85] M. Chiappalone, A. Vato, L. Berdondini, Et Al., Network Dynamics AndSynchronous Activity In Cultured Cortical Neurons, Int. J. Neural Syst.,2007,17(02):87-103.
    [86] S. Ghosh-Dastidar,H. Adeli, Spiking neural networks, Int J Neural Syst (In-ternational Journal of Neural Systems),2009,19(4):295-308.
    [87] M. Chen, C. S. Jiang, Q. X. Wu and W. H. Chen, Synchronization in ar-rays of uncertain delay neural networks by decentralized feedback control,(International Journal of Neural Systems),Int. J Neural Syst,2007,17(02):115-122.
    [88] J. Liang, Z. Wang and X. Liu, Global synchronization in an array of discrete-time neural networks with mixed coupling and time-varying delays, Inter-national Journal of Neural Systems,2009,19(1):57-63.
    [89] W. Wu and T. Chen, Asymptotic desynchronization for pulse-coupled os-cillators with delayed excitatory coupling, International Journal of NeuralSystems,2009,19(6):425-435.
    [90] N. Chakravarthy, S. Sabesan, K. Tsakalis and L.Iasemidis, Controling syn-chronization in a neurallevel population model,International Journal of Neu-ral Systems,2007,17(2):123-138.
    [91] L. B. Good, S. Sabesan, S. T. Marsh, K. S. Tsakalis and L. D. Iasemidis,Control of synchronization of brain dynamics leads to control of epilepticseizures in rodents, International Journal of Neural Systems,2009,19(3):173-196.
    [92] D. E. Postnov, L. S. Ryazanova, R. A. Zhirin, E. Mosekilde and O. V.Sosnovtseva, Noise controlled synchronization in potassium coupled neuralnetworks, International Journal of Neural Systems,2007,17(2):105-113.
    [93] H. Liljenstrom, Global efects of fuctuations in neural information process-ing, Int J Neural Syst,1996,7(4):497-505.
    [94] K. Wiesenfeld,F. Moss, Stochastic resonance and the benefts of noise: fromice ages to crayfsh and SQUIDs, Nature,1995,373(6509):33-36.
    [95] P. C. Gailey, A. Neiman, J. J. Collins, et al., Stochastic Resonance in En-sembles of Nondynamical Elements: The Role of Internal Noise, Phys RevLett,1997,79(23):4701-4704.
    [96] J.J. Collins, C.C. Chow, T.T. Imhof, Stochastic resonance without tuning,Nature,1995,376(6537):236-238.
    [97] A. Neiman, L. Schimansky-Geier, A. Cornell-Bell, et al., Noise-enhancedphase synchronization in excitable media, Phys Rev Lett,1999,83(23):4896-4899.
    [98] C. Zhou, J. Kurths, Noise-Induced Phase Synchronization and Synchroniza-tion Transitions in Chaotic Oscillators, Phys Rev Lett,2002,88(23):230602.
    [99] C. S. Zhou, J. Kurths, E. Allaria, et al., Constructive efects of noise inhomoclinic chaotic systems, Phys Rev E,2003,67(6):015205.
    [100] J. Csicsvari, H. Hirase, A. Czurko, et al., Oscillatory coupling of hippocam-pal pyramidal cells and interneurons in the behaving Rat, J Neurosci,1999,19(1):274-287.
    [101] M. Stopfer, S. Bhagavan, B. H. Smith, et al., Impaired odour discriminationon desynchronization of odour-encoding neural assemblies, Nature,1997,390(6655):70-74.
    [102] A. D. Reyes, Synchrony-dependent propagation of fring rate in iterativelyconstructed networks in vitro, Nat Neurosci,2003,6(6):593-599.
    [103] C. Martin, R. Gervais, E. Hugues, et al., Learning modulation of odor-induced oscillatory responses in the rat olfactory bulb: a correlate of odorrecognition?, J Neurosci,2004,24(2):389-397.
    [104] A. Riehle, S. Grün, M. Diesmann, et al., Spike Synchronization and RateModulation Diferentially Involved in Motor Cortical Function, Sci,1997,278(5345):1950-1953.
    [105] Y. Sakurai,S. Takahashi, Dynamic synchrony of fring in the monkey pre-frontal cortex during working-memory tasks, J Neurosci,2006,26(40):10141-10153.
    [106] Y. Ikegaya, G. Aaron, R. Cossart, et al., Synfre Chains and Cortical Songs:Temporal Modules of Cortical Activity, Sci,2004,304(5670):559-564.
    [107] A. K. Engel,W. Singer, Temporal binding and the neural correlates of sen-sory awareness, Trends Cogn Sci,2001,5(1):16-25.
    [108] W. Singer, Consciousness and the binding problem, Ann N Y Acad Sci,2001,929:123-146.
    [109]方锦清.复杂网络确定性模型研究的最新进展,复杂系统与复杂性科学,2008,5(4):29-46.
    [110]赵明,周涛,陈关荣,汪秉宏,复杂网络上动力系统同步的研究进展Ⅱ–如何提高网络的同步能力,物理学进展,2008,28(1):22-34.
    [111]赵明,汪秉宏,蒋品群,周涛,复杂网络上动力系统同步的研究进展,物理学进展,2005,25(3):273-295.
    [112] Ivan Soltesz, Kevin Staley, Computational Neuroscience in Epilepsy, Aca-demic Press, USA2008.
    [113] W. W. Lytton, Computer modelling of epilepsy, Nat Rev Neurosci,2008,9(8):626-637.
    [114] R. Albert,A.-L. Barabási, Statistical mechanics of complex networks, Rev.Mod. Phys.,2002,74(1):47-97.
    [115] M. E. J. Newman, The Structure and Function of Complex Networks,SIAMR,2003,45(2):167-256.
    [116] J. Zhang, J. Sun, X. Luo, et al., Characterizing pseudoperiodic time seriesthrough the complex network approach, Physica D: Nonlinear Phenomena,2008,237(22):2856-2865.
    [117] J. Zhang,M. Small, Complex Network from Pseudoperiodic Time Series:Topology versus Dynamics, Phys Rev Lett,2006,96(23):238701.
    [118] C. Rocsoreanu, A. Georgescu, and N. Giurgiteanu The FitzHugh-NagumoModel: Bifurcation and Dynamics. Kluwer Academic Publishers, Boston,(2000).
    [119] E. Labyt, P. Frogerais, L. Uva, J.J. Bellanger and F. Wendling, Mod-eling of Entorhinal Cortex and Simulation of Epileptic Activity: InsightsInto the Role of Inhibition-Related Parameters. Information Technology inBiomedicine, IEEE Transactions.2007,11(4):450-461.
    [120] E. M. Izhikevich, Dynamical Systems in Neuroscience: The Geometry ofExcitability and Bursting, the United State: The MIT Press,2005.
    [121] X. Li,J. Wang,W. Hu, Efects of chemical synapses on the enhancement ofsignal propagation in coupled neurons near the canard regime, Phys Rev EStat Nonlin Soft Matter Phys,2007,76(4Pt1):041902.
    [122] E. Salinas,T. J. Sejnowski, Correlated neuronal activity and the fow ofneural information, Nat Rev Neurosci,2001,2(8):539-550.
    [123] F. Fro¨hlich,D. A. McCormick, Endogenous Electric Fields May Guide Neo-cortical Network Activity, Neuron,2010,67(1):129-143.

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