Volume 50 Issue 1
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WANG Rubin, WANG Yihong, XU Xuying, PAN Xiaochuan. Mechanical thoughts and applications in cognitive neuroscience[J]. Advances in Mechanics, 2020, 50(1): 202012. doi: 10.6052/1000-0992-20-008
Citation: WANG Rubin, WANG Yihong, XU Xuying, PAN Xiaochuan. Mechanical thoughts and applications in cognitive neuroscience[J]. Advances in Mechanics, 2020, 50(1): 202012. doi: 10.6052/1000-0992-20-008

Mechanical thoughts and applications in cognitive neuroscience

doi: 10.6052/1000-0992-20-008
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  • Corresponding author: WANG Rubin
  • Received Date: 2020-04-17
  • Publish Date: 2020-10-08
  • This review article systematically summarizes the neural energy theory and methods proposed by our team in the field of brain science, and the internal relationship between mechanics and neural energy theory. This paper introduces how to construct an equivalent W-Z neuron model with the H-H model using the idea of analytic dynamics. Based on this, a large-scale neural model with neural energy as the core and a theoretical framework of global neural coding are proposed in the field of neuroscience. The unique functions and advantages of this novel neuron model are confirmed in the aspects of information processing, including visual perception, brain intelligence exploration, prediction of new working mechanisms of neurons and explanation of experimental phenomena challenging to explain in neuroscience. Because plasticity is the core of cognitive neuroscience and intelligent behavior, through the classical mechanical analysis of protein molecular machines, it is further clarified that the plasticity and neurodevelopment of neurons are not only biochemical reaction processes but also the role and contribution of mechanics are indispensable and important factors. It shows that the research thought of mechanics science in neuroscience and life science and its profound influence on internal logic. These studies will promote the integration of experimental neuroscience and theoretical neuroscience in the future, abandon the shortcomings in the research methods of reductionism and holism in the field of neuroscience, and integrate their respective advantages effectively. It is extremely important to promote the penetration of theories and methods of mechanical science.

     

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  • [1]
    董玮, 王如彬, 沈恩华 , 等. 2008. 节律性步态运动中CPG对肌肉的控制模式的仿真研究. 动力学与控制学报, 12:327-331

    (Dong W, Wang R B, Shen E H , et al. 2008. The simulation study on the pattern of muscles controlled by CPG in rhythm gait movement. Journal of Dynamics and Control, 12: 327-331).
    [2]
    顾凡及, 梁培基 . 2007. 神经信息处理. 北京: 北京工业大学出版社

    (Gu F J, Liang P J. 2007. Neural Information Processing. Beijing: Beijing University of Technology Press).
    [3]
    胡吉永, 丁辛, 王如彬 , 等. 2009. 触摸法评价织物柔软性的感知觉力学原理分析. 力学学报, 41:761-768

    (Hu J Y, Ding X, Wang R B , et al. 2009. Mechanictic principles of sensory analysis on fabric softness by touch means. Chinese Journal of Theoretical and Applied Mechanics, 41: 761-768).
    [4]
    胡吉永, 丁辛, 王如彬 , 等. 2012. 单纤维刺扎人体皮肤的弯曲力学行为分析. 动力学与控制学报, 10:162-167

    (Hu J Y, Ding X, Wang R B , et al. 2012. Bending mechanical behavior of single fiber prickling human skin. Journal of Dynamics and Control, 10: 162-167).
    [5]
    贾祥宇, 吴禹 . 2017. 动力学与生命科学的交叉研究进展综述. 动力学与控制学报, 15:279-288

    (Jia X Y, Wu Y . 2017. An overview on progress of interdisciplinary studies of dynamics and life sciences. Journal of Dynamics and Control, 15: 279-288).
    [6]
    刘亚宁 . 2002. 电磁生物效应. 北京: 北京邮电大学出版社

    (Liu Y N. 2002. Bioelectromagnetic Effects. Beijing: Beijing University of Posts and Telecommunications Press).
    [7]
    陆启韶, 刘深泉, 刘锋 , 等. 2008. 生物神经网络系统动力学与功能研究. 力学进展, 38:766-793

    (Lu Q S, Liu S Q, Liu F , et al. 2008. Research on dynamics and function of biological neural network systems. Advances in Mechanics, 38: 766-793).
    [8]
    陆启韶 . 2020. 神经动力学与力学. 动力学与控制学报, 18:6-10

    (Lu Q S . 2020. Neurodynamics and mechanics. Journal of Dynamics and Control, 18: 6-10).
    [9]
    彭俊, 王如彬 . 2019. 大脑血液动力学中的神经能量编码. 力学学报, 51:1202-1209

    (Peng J, Wang R B . 2019. Energy coding of hemodynamic phenomena in the brain. Chinese Journal of Theoretical and Applied Mechanics, 51: 1202-1209).
    [10]
    彭俊, 王如彬 . 2020. 神经元膜电位对信息的编码. 动力学与控制学报, 18:24-32

    (Peng J, Wang R B . 2020. Information coding of neuronal membrane potential. Journal of Dynamics and Control, 18: 24-32).
    [11]
    戎伟峰, 王如彬 . 2019. 耳蜗毛细胞活动的神经动力学分析. 应用数学和力学, 40:139-149

    (Rong W F, Wang R B . 2019. Neurodynamics analysis of cochlear hair cell activity. Applied Mathematics and Mechanics, 40: 139-149).
    [12]
    汪云九 . 2006. 神经信息学. 北京: 高等教育出版社

    (Wang Y J. 2006. Neuroninformatics. Beijing: Higher Education Press).
    [13]
    王如彬, 张志康 . 2012. 基于信息编码的神经能量计算. 力学学报, 44:779-786

    (Wang R B, Zhang Z K . 2012. Computation of neuronal energy based on information coding. Chinese Journal of Theoretical and Applied Mechanics, 44: 779-786).
    [14]
    王如彬, 周轶, 张志康 . 2011. 具有延时作用的基底膜主动耦合模型. 振动与冲击, 30:49-53, 73

    (Wang R B, Zhou Y, Zhang Z K . 2011. An active coupling model for basilar membrane with time-delay action. Journal of Vibration and Shock, 30: 49-53, 73).
    [15]
    王如彬, 张志康 . 2008. 耦合条件下大脑坡层神经振子群的能量函数. 力学学报, 40:238-249

    (Wang R B, Zhang Z K . 2008. Energy funation of population of neural oscillators in cerebral cortex under coupling condition. Chinese Journal of Theoretical and Applied Mechanics, 40: 238-249).
    [16]
    王如彬 . 2020. 神经动力学研究进展. 动力学与控制学报, 18:1-5

    (Wang R B . 2020. Research advances in neurodynamics. Journal of Dynamics and Control, 18: 1-5).
    [17]
    武田晓 . 1999. 脑和物理学. 东京: 裳华房株式会社

    (Takata G . 1999. Brain and Physics. Tokyo: Shokabo Company).
    [18]
    张健鹏, 王如彬, 沈恩华 , 等. 2009. 关于昆虫步态运动时神经控制机理的动力学分析. 动力学与控制学报, 7:29-34

    (Zhang J P, Wang R B, Shen E H , et al. 2009. An exploration of dynamics on neural control mechanism of insect locomotion. Journal of Dynamics and Control, 7: 29-34).
    [19]
    张健鹏, 王如彬 . 2009. 基于被动力学的昆虫运动动力学的建模与分析. 力学季刊, 1:39-43

    (Zhang J P, Wang R B . 2009. Modeling and dynamic analysis of insect locomotion based on passive dynamics. Chinese Quarterly of Mechanics, 1: 39-43).
    [20]
    郑锦超, 王如彬 . 2012. 神经能量与神经信息之间内在动力学初探. 力学学报, 44:919-927

    (Zheng J C, Wang R B . 2012. The first exploration of the dynamic relation between nervous energy and neueal information. Chinese Journal of Theoretical and Applied Mechanics, 44: 919-927).
    [21]
    Abbasi S, Maran S, Jaeger D. 2020. A general method to generate artificial spike train populations matching recorded neurons. Journal of Computational Neuroscience, 48:47-63.
    [22]
    Baker J E, Brust-Mascher I, Ramachandran S , et al. 1998. A large and distinct rotation of the myosin light chain domain occurs upon muscle contraction. Proceedings of the National Academy of Sciences of the United States of America, 95:2944-2949.
    [23]
    Basar E. 1998. Brain Function and Oscillations. Berlin: Springer.
    [24]
    Basar E. 2011. Brain-Body-Mind in the Nebulous Cartesian System: A Holistic Approach by Oscillations. Berlin:Springer.
    [25]
    Bergmann Tiest W M, Kappers A. 2009. Cues for haptic perception of compliance. IEEE Transactions on Haptics, 2:189-199.
    [26]
    Betz T, Lim D, Kas J A. 2006. Neuronal growth: A bistable stochastic process. Physical Review Letters, 96:098103.
    [27]
    Bonzon P. 2017. Towards neuro-inspired symbolic models of cognition: Linking neural dynamics to behaviors through asynchronous communications. Cognitive Neurodynamics, 11:327-353.
    [28]
    Brown A M. 2004. Brain glycogen re-awakened. Journal of Neurochemistry, 89:537-552.
    [29]
    Brown A M, Baltan Tekkok S, Ransom B R. 2004. Energy transfer from astrocytes to axons: The role of CNS glycogen. Neurochemistry International, 45:529-536.
    [30]
    Bullmore E, Sporns O. 2009. Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews. Neuroscience, 10:186-198.
    [31]
    Buxton R B. 2012. Dynamic models of BOLD contrast. NeuroImage, 62:953-961.
    [32]
    Byrne J H, Roberts J L. 2009. From Molecules to Networks. Amsterdam: Elsevier.
    [33]
    Chen M, Guo D, Li M , et al. 2015. Critical roles of the direct gabaergic pallido-cortical pathway in controlling absence seizures. PLoS Computational Biology, 11:e1004539.
    [34]
    Chen M, Guo D, Wang T , et al. 2014. Bidirectional control of absence seizures by the basal ganglia: A computational evidence. PLoS Computational Biology, 10:e1003495.
    [35]
    Churchland M M, Cunningham J P, Kaufman M T , et al. 2012. Neural population dynamics during reaching. Nature, 487:51-56.
    [36]
    Clancy K, Ding M, Bernat E , et al. 2017. Restless "rest": Intrinsic sensory hyperactivity and disinhibition in post-traumatic stress disorder. Brain: A Journal of Neurology, 140:2041-2050.
    [37]
    Cooke R. 1998. New angle on myosin. Proceedings of the National Academy of Sciences of the United States of America, 95:2720-2722.
    [38]
    Deco G, Jirsa V, McIntosh A R , et al. 2009. Key role of coupling, delay, and noise in resting brain fluctuations. Proceedings of the National Academy of Sciences of the United States of America, 106:10302-10307.
    [39]
    Dinuzzo M, Mangia S, Maraviglia B , et al. 2012. The role of astrocytic glycogen in supporting the energetics of neuronal activity. Neurochemical Research, 37:2432-2438.
    [40]
    Dong W, Wang R. 2011. Exploring human rhythmic gait movement in the role of cerebral cortex signal. Applied Mathematics and Mechanics, 32:223-230.
    [41]
    Du Y, Wang R, Han F , et al. 2015. The parameter-dependent synchronization of coupled neurons in cold receptor model. International Journal of Non-Linear Mechanics, 70:95-104.
    [42]
    Eikenberry S E, Marmarelis V Z. 2015. Principal dynamic mode analysis of the Hodgkin-Huxley equations. International Journal of Neural Systems, 25:1550001.
    [43]
    Epstein R. 2016. The empty brain. AEON Essays, 2016-05-25.
    [44]
    Erdogdu E, Kurt E, Duru A D , et al. 2019. Measurement of cognitive dynamics during video watching through event-related potentials (ERPs) and oscillations (EROs). Cognitive Neurodynamics, 13:503-512.
    [45]
    Ermentrout G B, Galan R F, Urban N N. 2007. Relating neural dynamics to neural coding. Physical Review Letters, 99:248103.
    [46]
    Evans E A, Hochmuth R M. 1976. Membrane viscoelasticity. Biophysical Journal, 16:1-11.
    [47]
    Fan D, Wang Q. 2018. Improved control effect of absence seizures by autaptic connections to the subthalamic nucleus. Physical Review E, 98:052414.
    [48]
    Fan D, Wang Q, Su J , et al. 2017. Stimulus-induced transitions between spike-wave discharges and spindles with the modulation of thalamic reticular nucleus. Journal of Computational Neuroscience, 43:203-225.
    [49]
    Fan D, Wang Z, Wang Q. 2016. Optimal control of directional deep brain stimulation in the parkinsonian neuronal network. Communications in Nonlinear Science and Numerical Simulation, 36:219-237
    [50]
    Fan D, Zhang L, Wang Q. 2018. Transition dynamics and adaptive synchronization of time-delay interconnected corticothalamic systems via nonlinear control. Nonlinear Dynamics, 94:2807-2825.
    [51]
    Fan H, Pan X, Wang R , et al. 2017. Differences in reward processing between putative cell types in primate prefrontal cortex. PloS One, 12:e0189771.
    [52]
    Figley C R, Stroman P W. 2011. The role(s) of astrocytes and astrocyte activity in neurometabolism, neurovascular coupling, and the production of functional neuroimaging signals. The European Journal of Neuroscience, 33:577-588.
    [53]
    Fox M D, Raichle M E. 2007. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Reviews. Neuroscience, 8:700-711.
    [54]
    Freeman W. 2000. Neurodynamics. Berlin: Springer.
    [55]
    Gazzaniga M S, Ivry R B, Mangun G R. 2002. Cognitive Neuroscience. London: W. W. Norton & Company.
    [56]
    Gazzaniga M S, Ivry R B, Mangun G R. 2009. Cognitive Neuroscience. London: W. W. Norton & Company.
    [57]
    Gerling G J, Thomas G W. 2008. Fingerprint lines may not directly affect SA-I mechanoreceptor response. Somatosensory & Motor Research, 25:61-76.
    [58]
    Guclu B, Mahoney G K, Pawson L J , et al. 2008. Localization of Merkel cells in the monkey skin: An anatomical model. Somatosensory & Motor Research, 25:123-138.
    [59]
    Guo D, Wang Q, Perc M. 2012. Complex synchronous behavior in interneuronal networks with delayed inhibitory and fast electrical synapses. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics, 85:061905.
    [60]
    Haken H. 1996. Principles of Brain Functioning. Berlin: Springer.
    [61]
    Harvey M A, Saal H P, Dammann J F , et al. 2013. Multiplexing stimulus information through rate and temporal codes in primate somatosensory cortex. PLoS Biology, 11:e1001558.
    [62]
    Hayashi H. 1998. Nonlinear Phenomenon of Neural Systems: Kolona Press (in Japanese).
    [63]
    Hipp J F, Engel A K, Siegel M. 2011. Oscillatory synchronization in large-scale cortical networks predicts perception. Neuron, 69:387-396.
    [64]
    Holt J R, Corey D P. 2000. Two mechanisms for transducer adaptation in vertebrate hair cells. Proceedings of the National Academy of Sciences of the United States of America, 97:11730-11735.
    [65]
    Hopfield J J. 2010. Neurodynamics of mental exploration. Proceedings of the National Academy of Sciences of the United States of America, 107:1648-1653.
    [66]
    Hoyer S. 1992. Oxidative energy metabolism in Alzheimer brain. Studies in early-onset and late-onset cases. Molecular and Chemical Neuropathology, 16:207-224.
    [67]
    Hu J, Ding X, Wang R. 2007. Biomechanical mechanism of fabric softness discrimination. Fibers and Polymers, 8:372-376.
    [68]
    Hu J, Ding X, Wang R. 2009. Intrinsic differences of sensory analysis from instrumental evaluation on fabric softness by lateral compression. Fibers and Polymers, 10:371-378.
    [69]
    Hu J, Li Y, Ding X , et al. 2011. The mechanics of buckling fiber in relation to fabric-evoked prickliness-a theory model of single fiber prickling human skin. Journal of The Textile Institute, 102:1003-1018.
    [70]
    Hu J, Li Y, Hu J. 2010. Neuromechanical representation of fabric-evoked prickle: Spatial and probability integration. Fibers and Polymers, 11:790-797.
    [71]
    Hu J, Yang X, Ding X. 2012. Probability of prickliness detection in a model of populations of fiber ends prickling human skin. Fibers and Polymers, 13:79-86.
    [72]
    Hu J, Zhang X, Yang X , et al. 2016. Analysis of fingertip/fabric friction-induced vibration signals toward vibrotactile rendering. The Journal of The Textile Institute, 107:967-975.
    [73]
    Hu J, Zhao Q, Jiang R , et al. 2013. Responses of cutaneous mechanoreceptors within fingerpad to stimulus information for tactile softness sensation of materials. Cognitive Neurodynamics, 7:441-447.
    [74]
    Iribarren J L, Moro E. 2009. Impact of human activity patterns on the dynamics of information diffusion. Physical Review Letters, 103:038702.
    [75]
    Ji X, Hu X, Zhou Y , et al. 2019. Adaptive sparse coding based on memristive neural network with applications. Cognitive Neurodynamics, 13:475-488.
    [76]
    Jia B, Gu H. 2017. Dynamics and physiological roles of stochastic neural firing patterns near bifurcation points. International Journal of Bifurcation and Chaos, 27:1750113.
    [77]
    Jia B, Gu H, Xue L. 2017. A basic bifurcation structure from bursting to spiking of injured nerve fibers in a two-dimensional parameter space. Cognitive Neurodynamics, 11:189-200.
    [78]
    Jiang J, Bramao I, Khazenzon A , et al. 2020. Temporal dynamics of memory-guided cognitive control and generalization of control via overlapping associative memories. The Journal of Neuroscience: The official Journal of the Society for Neuroscience, 40:2343-2356.
    [79]
    Jiang R, Hu J, Ding X. 2016. Analysis of fingertip textile friction induced vibration by time-frequency method. Fibers and Polymers, 17:430-436.
    [80]
    Jiyong H, Yi L, Xin D , et al. 2011. Neuromechanical representation of fabric-evoked prickliness: A fiber-skin-neuron model. Cognitive Neurodynamics, 5:161-170.
    [81]
    Johansson R S, Flanagan J R. 2009. Coding and use of tactile signals from the fingertips in object manipulation tasks. Nature Reviews. Neuroscience, 10:345-359.
    [82]
    Kim S S, Sripati A P, Bensmaia S J. 2010. Predicting the timing of spikes evoked by tactile stimulation of the hand. Journal of Neurophysiology, 104:1484-1496.
    [83]
    Kim S Y, Lim W. 2018. Effect of spike-timing-dependent plasticity on stochastic burst synchronization in a scale-free neuronal network. Cognitive Neurodynamics, 12:315-342.
    [84]
    Kim S Y, Lim W. 2019. Burst synchronization in a scale-free neuronal network with inhibitory spike-timing-dependent plasticity. Cognitive Neurodynamics, 13:53-73.
    [85]
    Lakatos P, Karmos G, Mehta A D , et al. 2008. Entrainment of neuronal oscillations as a mechanism of attentional selection. Science, 320:110-113.
    [86]
    Laughlin S, Sejnowski T. 2003. Communication in neural networks. Science China Technological Sciences, 301:1870.
    [87]
    Laughlin S B. 2001. Energy as a constraint on the coding and processing of sensory information. Current Opinion in Neurobiology, 11:475-480.
    [88]
    Levy W B, Baxter R A. 1996. Energy efficient neural codes. Neural Computation, 8:531-543.
    [89]
    Li C Y, Poo M M, Dan Y. 2009. Burst spiking of a single cortical neuron modifies global brain state. Science, 324:643-646.
    [90]
    Li H, Sun X, Xiao J. 2018. Stochastic multiresonance in coupled excitable FHN neurons. Chaos, 28:043113.
    [91]
    Li X, Luo S, Xue F. 2020. Effects of synaptic integration on the dynamics and computational performance of spiking neural network. Cognitive Neurodynamics, 14:347-357.
    [92]
    Lin A L, Fox P T, Hardies J , et al. 2010. Nonlinear coupling between cerebral blood flow, oxygen consumption, and ATP production in human visual cortex. Proceedings of the National Academy of Sciences of the United States of America, 107:8446-8451.
    [93]
    Lu Q, Gu H, Yang Z , et al. 2008. Dynamics of firing patterns, synchronization and resonances in neuronal electrical activities: Experiments and analysis. Acta Mech Sinica, 24:593-628.
    [94]
    Lumpkin E A, Caterina M J. 2007. Mechanisms of sensory transduction in the skin. Nature, 445:858-865.
    [95]
    Lv M, Wang C, Ren G , et al. 2016 a. Model of electrical activity in a neuron under magnetic flow effect. Nonlinear Dynamics, 85:1479-1490.
    [96]
    Lv M, Wang C, Ren G , et al. 2016 b. Model of electrical activity in a neuron under magnetic flow effect. Nonlinear Dynamics, 85:1479-1490.
    [97]
    Ma D, Feng L, Cheng Y , et al. 2018. Astrocytic gap junction inhibition by carbenoxolone enhances the protective effects of ischemic preconditioning following cerebral ischemia. Journal of Neuroinflammation, 15:198.
    [98]
    Ma J, Tang J. 2017. A review for dynamics in neuron and neuronal network. Nonlinear Dynamics, 89:1569-1578.
    [99]
    Ma J, Wu F, Hayat T. 2017. Electromagnetic induction and radiation-induced abnormality of wave propagation in excitable media. Physica A Statistical Mechanics & Its Applications, 486:508-516.
    [100]
    Ma J, Yang Z Q, Yang L J , et al. 2019. A physical view of computational neurodynamics. Journal of Zhejiang University—Science A, 20:639-659.
    [101]
    Maandag N J, Coman D, Sanganahalli B G , et al. 2007. Energetics of neuronal signaling and fMRI activity. Proceedings of the National Academy of Sciences of the United States of America, 104:20546-20551.
    [102]
    Maksimovic S, Nakatani M, Baba Y , et al. 2014. Epidermal Merkel cells are mechanosensory cells that tune mammalian touch receptors. Nature, 509:617-621.
    [103]
    Malarkey E B, Parpura V. 2008. Mechanisms of glutamate release from astrocytes. Neurochemistry International, 52:142-154.
    [104]
    McIntyre J, Zago M, Berthoz A , et al. 2001. Does the brain model Newton's laws? Nature Neuroscience, 4:693-694.
    [105]
    Memmesheimer R M, Timme M. 2006. Designing the dynamics of spiking neural networks. Physical Review Letters, 97:188101.
    [106]
    Mitrossilis D, Fouchard J, Guiroy A , et al. 2009. Single-cell response to stiffness exhibits muscle-like behavior. Proceedings of the National Academy of Sciences of the United States of America, 106:18243-18248.
    [107]
    Mondal A, Upadhyay R K, Ma J , et al. 2019. Bifurcation analysis and diverse firing activities of a modified excitable neuron model. Cognitive Neurodynamics, 13:393-407.
    [108]
    Moore C I, Cao R. 2008. The hemo-neural hypothesis: On the role of blood flow in information processing. Journal of Neurophysiology, 99:2035-2047.
    [109]
    Mora-Sanchez A, Dreyfus G, Vialatte F B. 2019. Scale-free behaviour and metastable brain-state switching driven by human cognition, an empirical approach. Cognitive Neurodynamics, 13:437-452.
    [110]
    Nimmy John T, Subha D P, Menon R. 2018. Analysis of long range dependence in the EEG signals of Alzheimer patients. Cognitive Neurodynamics, 12:183-199.
    [111]
    Oprea L, Pack C C, Khadra A. 2020. Machine classification of spatiotemporal patterns: Automated parameter search in a rebounding spiking network. Cognitive Neurodynamics, 14:267-280.
    [112]
    Pan X, Fan H, Sawa K , et al. 2014. Reward inference by primate prefrontal and striatal neurons. The Journal of Neuroscience: The official Journal of the Society for Neuroscience, 34:1380-1396.
    [113]
    Parastesh F, Rajagopal K, Karthikeyan A , et al. 2018. Complex dynamics of a neuron model with discontinuous magnetic induction and exposed to external radiation. Cognitive Neurodynamics, 12:607-614.
    [114]
    Pellerin L, Magistretti P J. 1994. Glutamate uptake into astrocytes stimulates aerobic glycolysis: A mechanism coupling neuronal activity to glucose utilization. Proceedings of the National Academy of Sciences of the United States of America, 91:10625-10629.
    [115]
    Peng J, Wang Y, Wang R , et al. 2020. Neural coupling mechanism in fMRI hemodynamics. Nonlinear Dynamics.
    [116]
    Peppiatt C, Attwell D. 2004. Neurobiology: Feeding the brain. Nature, 431:137-138.
    [117]
    Pouget A, Latham P. 2002. Digitized neural networks: Long-term stability from forgetful neurons. Nature Neuroscience, 5:709-710.
    [118]
    Qin S, Yin H, Yang C , et al. 2016. A magnetic protein biocompass. Nature materials, 15:217-226.
    [119]
    Qu J, Wang R. 2017. Collective behavior of large-scale neural networks with GPU acceleration. Cognitive Neurodynamics, 11:553-563.
    [120]
    Qu J, Wang R, Yan C , et al. 2017. Spatiotemporal behavior of small-world neuronal networks using a map-based model. Neural Processing Letters, 45:689-701.
    [121]
    Rabinovich M I, Huerta R, Afraimovich V. 2006. Dynamics of sequential decision making. Physical Review Letters, 97:188103.
    [122]
    Raichle M E, Gusnard D A. 2002. Appraising the brain's energy budget. Proceedings of the National Academy of Sciences of the United States of America, 99:10237-10239.
    [123]
    Rangan A V, Cai D, McLaughlin D W. 2008. Quantifying neuronal network dynamics through coarse-grained event trees. Proceedings of the National Academy of Sciences of the United States of America, 105:10990-10995.
    [124]
    Rao A R. 2018. An oscillatory neural network model that demonstrates the benefits of multisensory learning. Cognitive Neurodynamics, 12:481-499.
    [125]
    Rauzi M, Verant P, Lecuit T , et al. 2008. Nature and anisotropy of cortical forces orienting Drosophila tissue morphogenesis. Nature Cell Biology, 10:1401-1410.
    [126]
    Retamal M A, Schalper K A, Shoji K F , et al. 2007. Possible involvement of different connexin43 domains in plasma membrane permeabilization induced by ischemia-reperfusion. The Journal of Membrane Biology, 218:49-63.
    [127]
    Rong W, Wang R, Zhang J , et al. 2020. Neurodynamics analysis of cochlear cell activity. Theoretical & Applied Mechanics Letters, 18:1-5.
    [128]
    Rubinov M, Sporns O, Thivierge J P , et al. 2011. Neurobiologically realistic determinants of self-organized criticality in networks of spiking neurons. PLoS Computational Biology, 7:e1002038.
    [129]
    Sandrini M, Cohen L G, Censor N. 2015. Modulating reconsolidation: A link to causal systems-level dynamics of human memories. Trends in Cognitive Sciences, 19:475-482.
    [130]
    Schwarz Henriques S, Sandmann R, Strate A , et al. 2012. Force field evolution during human blood platelet activation. Journal of Cell Science, 125:3914-3920.
    [131]
    Seyfried T N, Kiebish M, Mukherjee P , et al. 2008. Targeting energy metabolism in brain cancer with calorically restricted ketogenic diets. Epilepsia, 49 Suppl 8: 114-116.
    [132]
    Sokoloff L. 2008. The physiological and biochemical bases of functional brain imaging. Cognitive Neurodynamics, 2:1-5.
    [133]
    Stender J, Mortensen K N, Thibaut A , et al. 2016. The minimal energetic requirement of sustained awareness after brain injury. Current Biology: CB, 26:1494-1499.
    [134]
    Sun X, Lei J, Perc M , et al. 2011. Burst synchronization transitions in a neuronal network of subnetworks. Chaos, 21:016110.
    [135]
    Sun X, Perc M, Kurths J , et al. 2018. Fast regular firings induced by intra- and inter-time delays in two clustered neuronal networks. Chaos, 28:106310.
    [136]
    Talebi N, Nasrabadi A M, Mohammad-Rezazadeh I. 2018. Estimation of effective connectivity using multi-layer perceptron artificial neural network. Cognitive Neurodynamics, 12:21-42.
    [137]
    Talhouk R S, Zeinieh M P, Mikati M A , et al. 2008. Gap junctional intercellular communication in hypoxia-ischemia-induced neuronal injury. Progress in Neurobiology, 84:57-76.
    [138]
    Tass P A. 1999. Phase Resetting in Medicine and Biology. Berlin: Springer.
    [139]
    Tejo M, Araya H, Niklitschek-Soto S , et al. 2019. Theoretical models of reaction times arising from simple-choice tasks. Cognitive Neurodynamics, 13:409-416.
    [140]
    Videbech P. 2000. PET measurements of brain glucose metabolism and blood flow in major depressive disorder: A critical review. Acta Psychiatrica Scandinavica, 101:11-20.
    [141]
    Wang D, Wang C, Liu L , et al. 2018. Protective effects of evodiamine in experimental paradigm of Alzheimer's disease. Cognitive Neurodynamics, 12:303-313.
    [142]
    Wang G, Wang R. 2017. Sparse coding network model based on fast independent component analysis. Neural Computing & Applications, 13:1-7.
    [143]
    Wang G, Wang R, Kong W , et al. 2018. Simulation of retinal ganglion cell response using fast independent component analysis. Cognitive Neurodynamics, 12:615-624.
    [144]
    Wang J, Yang X, Sun Z. 2018. Suppressing bursting synchronization in a modular neuronal network with synaptic plasticity. Cognitive Neurodynamics, 12:625-636.
    [145]
    Wang R, Hayashi H, Zhang Z , et al. 2003. An exploration of dynamics on moving mechanism of the growth cone. Molecules, 8:127-138.
    [146]
    Wang R, Tsuda I, Zhang Z. 2015. A new work mechanism on neuronal activity. International Journal of Neural Systems, 25:1450037.
    [147]
    Wang R, Wang G, Zheng J. 2014. An exploration of the range of noise intensity that affects the membrane potential of neurons. Abstract and Applied Analysis, 801642.
    [148]
    Wang R, Wang Z. 2018. The essence of neuronal activity from the consistency of two different neuron models. Nonlinear Dynamics, 92:973-982.
    [149]
    Wang R, Zhang Z. 2006. Mechanism on brain information processing: Energy coding. Applied Physical Letters, 89:123903.
    [150]
    Wang R, Zhang Z, Chen G. 2008. Energy function and energy evolution on neuronal populations. IEEE Transactions on Neural Networks, 19:535-538.
    [151]
    Wang R, Zhang Z, Chen G. 2009. Energy coding and energy functions for local activities of brain. Neurocomputing, 73:139-150
    [152]
    Wang R, Zhang Z, Qu J , et al. 2011. Phase synchronization motion and neural coding in dynamic transmission of neural information. IEEE Transactions on Neural Networks, 22:1097-1106.
    [153]
    Wang R, Zhang Z, Tee C K. 2009. Neurodynamics analysis on transmission of brain information. Applied Mathematics and Mechanics, 30:1415-1428
    [154]
    Wang R, Zhu Y. 2016. Can the activities of the large scale cortical network be expressed by neural energy? A brief review. Cognitive Neurodynamics, 10:1-5.
    [155]
    Wang W, Wang R. 2016. Control strategy of CPG gait movement under the condition of attention selection. Applied Mathematics and Mechanics, 37:957-966.
    [156]
    Wang Y, Wang R. 2018. An improved neuronal energy model that better captures of dynamic property of neuronal activity. Nonlinear Dynamics, 91:319-327.
    [157]
    Wang Y, Wang R, Xu X. 2017. Neural energy supply-consumption properties based on Hodgkin-Huxley model. Neural Plasticity, 2017: 6207141.
    [158]
    Wang Y, Wang R, Zhu Y. 2017. Optimal path-finding through mental exploration based on neural energy field gradients. Cognitive Neurodynamics, 11:99-111.
    [159]
    Wang Y, Xu X, Wang R. 2018 a. An energy model of place cell network in three dimensional space. Frontiers in Neuroscience, 12:264.
    [160]
    Wang Y, Xu X, Wang R. 2018 b. Intrinsic sodium currents and excitatory synaptic transmission influence spontaneous firing in up and down activities. Neural Networks: The Official Journal of the International Neural Network Society, 98:42-50.
    [161]
    Wang Y, Xu X, Wang R. 2019. The place cell activity is information-efficient constrained by energy. Neural Networks: The Official Journal of the International Neural Network Society, 116:110-118.
    [162]
    Wang Y, Xu X, Wang R. 2020. Energy features in spontaneous up and down oscillations. Cognitive Neurodynamics, https://doi.org/10.1007/s11571-020-09597-3.
    [163]
    Wang Y, Xu X, Zhu Y , et al. 2019. Neural energy mechanism and neurodynamics of memory transformation. Nonlinear Dynamics, 97:697-714.
    [164]
    Wang Z, Kai L, Day M , et al. 2006. Dopaminergic control of corticostriatal long-term synaptic depression in medium spiny neurons is mediated by cholinergic interneurons. Neuron, 50:443-452.
    [165]
    Wang Z, Wang R. 2014. Energy distribution property and energy coding of a structural neural network. Frontiers in Computational Neuroscience, 8:14.
    [166]
    Wang Z, Wang R, Fang R. 2015. Energy coding in neural network with inhibitory neurons. Cognitive Neurodynamics, 9:129-144.
    [167]
    Wu F, Wang C, Xu Y , et al. 2016. Model of electrical activity in cardiac tissue under electromagnetic induction. Scientific Reports, 6:28.
    [168]
    Wu S, Zhang Y, Cui Y , et al. 2019. Heterogeneity of synaptic input connectivity regulates spike-based neuronal avalanches. Neural Networks: The Official Journal of the International Neural Network Society, 110:91-103.
    [169]
    Xu X, Ni L, Wang R. 2016. A neural network model of spontaneous up and down transitions. Nonlinear Dynamics, 84:1541-1551.
    [170]
    Xu X, Ni L, Wang R. 2017. Synchronous transitions of up and down states in a network model based on stimulations. Journal of Theoretical Biology, 412:130-137.
    [171]
    Yanagida T, Iwane A H. 2000. A large step for myosin. Proceedings of the National Academy of Sciences of the United States of America, 97:9357-9359.
    [172]
    Yang C, Luan G, Wang Q , et al. 2018. Localization of epileptogenic zone with the correction of pathological networks. Frontiers in Neurology, 9:143.
    [173]
    Yang X, Hu J, Ding X , et al. 2014. Capability and limitation in evaluation on perceived fabric softness by three types of sensory modality. Fibers and Polymers, 15:2651-2657.
    [174]
    Yao M, Wang R. 2019. Neurodynamic analysis of Merkel cell-neurite complex transduction mechanism during tactile sensing. Cognitive Neurodynamics, 13:293-302.
    [175]
    Yao Y, Ma J. 2018. Weak periodic signal detection by sine-Wiener-noise-induced resonance in the FitzHugh-Nagumo neuron. Cognitive Neurodynamics, 12:343-349.
    [176]
    Yin X, Wang R. 2016. Simulation of dopamine modulation-based memory model. Neurocomputing, 194:241-245.
    [177]
    Yu Y, Hao Y, Wang Q. 2020. Model-based optimized phase-deviation deep brain stimulation for Parkinson's disease. Neural Networks: The official Journal of the International Neural Network Society, 122:308-319.
    [178]
    Zhan F, Liu S. 2019. A Hénon-like map inspired by the generalized discrete-time FitzHugh-Nagumo model. Nonlinear Dynamics, 97:2675-2691.
    [179]
    Zhan F, Liu S, Zhang X , et al. 2018. Mixed-mode oscillations and bifurcation analysis in a pituitary model. Nonlinear Dynamics, 94:807-826.
    [180]
    Zhang H, Su J, Wang Q , et al. 2018. Predicting seizure by modeling synaptic plasticity based on EEG signals—a case study of inherited epilepsy. Communications in Nonlinear Science & Numerical Simulation, 56:330-343.
    [181]
    Zhang T, Pan X, Xu X , et al. 2019. A cortical model with multi-layers to study visual attentional modulation of neurons at the synaptic level. Cognitive Neurodynamics, 13:579-599.
    [182]
    Zhang X, Liu S, Zhan F , et al. 2017. The effects of medium spiny neuron morphologcial changes on basal ganglia network under external electric field: A computational modeling study. Frontiers in Computational Neuroscience, 11:91.
    [183]
    Zhang Y, Pan X, Wang R , et al. 2016. Functional connectivity between prefrontal cortex and striatum estimated by phase locking value. Cognitive Neurodynamics, 10:245-254.
    [184]
    Zhao Z, Li L, Gu H. 2018. Dynamical mechanism of hyperpolarization-activated non-specific cation current induced resonance and spike-timing precision in a neuronal model. Frontiers in Cellular Neuroscience, 12:62.
    [185]
    Zheng H, Wang R, Qiao L , et al. 2014. The molecular dynamics of neural metabolism during the action potential. Science China Technological Sciences, 57:857-863.
    [186]
    Zheng H, Wang R, Qu J. 2016. Effect of different glucose supply conditions on neuronal energy metabolism. Cognitive Neurodynamics, 10:563-571.
    [187]
    Zheng Z, Wang R. 2017. Arm motion control model based on central pattern generator. Applied Mathematics and Mechanics, 38:1247-1256.
    [188]
    Zhong H, Wang R. 2020. Neural mechanism of degradation of visual information data from retina to V1 area. Cognitive Neurodynamics, https://doi.org/10.1007/s11571-020-09599-1.
    [189]
    Zhu F, Wang R, Aihara K , et al. 2020. Energy-efficient firing patterns with sparse bursts in the chay neuron model. Nonlinear Dynamics, 100: 2657-2672. https://doi.org/10.1007/s11071-020-05593-8.
    [190]
    Zhu F, Wang R, Pan X , et al. 2019. Energy expenditure computation of a single bursting neuron. Cognitive Neurodynamics, 13:75-87.
    [191]
    Zhu Y, Wang R, Wang Y. 2016. A comparative study of the impact of theta-burst and high-frequency stimulation on memory performance. Frontiers in Human Neuroscience, 10:19.
    [192]
    Zhu Y, Wang R, Wang Y. 2016. The impact of theta-burst stimulation on memory mechanism: A modeling study. Applied Mathematics and Mechanics, 37:395-402.
    [193]
    Zhu Z, Wang R, Zhu F. 2018. The energy coding of a structural neural network based on the Hodgkin-Huxley model. Frontiers in Neuroscience, 12:122.
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