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王如彬 张志康. 耦合条件下大脑皮层神经振子群的能量函数[J]. 力学学报, 2008, 40(2): 238-249. DOI: 10.6052/0459-1879-2008-2-2007-115
引用本文: 王如彬 张志康. 耦合条件下大脑皮层神经振子群的能量函数[J]. 力学学报, 2008, 40(2): 238-249. DOI: 10.6052/0459-1879-2008-2-2007-115
Rubin Wang, Zhikang Zhang. Energy function of population of neural oscillators in cerebral cortex under coupling condition[J]. Chinese Journal of Theoretical and Applied Mechanics, 2008, 40(2): 238-249. DOI: 10.6052/0459-1879-2008-2-2007-115
Citation: Rubin Wang, Zhikang Zhang. Energy function of population of neural oscillators in cerebral cortex under coupling condition[J]. Chinese Journal of Theoretical and Applied Mechanics, 2008, 40(2): 238-249. DOI: 10.6052/0459-1879-2008-2-2007-115

耦合条件下大脑皮层神经振子群的能量函数

Energy function of population of neural oscillators in cerebral cortex under coupling condition

  • 摘要: 探讨了局部脑皮层网络活动中,耦合条件下的大规模神经振子群的能量消耗与神经信号编码之间的内禀关系,得到了神经元集群在阈下和阈上互相耦合时神经元膜电位变化的函数. 这个能量函数能够精确地再现神经电生理学实验中的EPSP,IPSP,动作电位以及动作电流. 最近功能性核磁共振实验证明了神经信号的编码是与能量的消耗紧密地耦合在一起的,因此研究结果表明利用能量原理研究大脑在神经网络层次上是如何进行编码的这一重大科学问题的讨论是十分有益的. 可以预计得到的能量函数将是生物学神经网络动力学稳定性计算的基础.

     

    Abstract: This paper explores the intrinsic relationship betweenenergy consumption of a large scale neural population and neural signalprocessing under coupling condition in neural networks in activity of localbrain, and energy functions of variety of the neuronal membrane potentialare obtained for interactive neural population at the sub-threshold and thesupra-threshold states. These energy functions can accurately reproduceexcitatory postsynaptic potentials (EPSP), inhibitory postsynapticpotentials (IPSP), action potential, and action potential given by theneuro-electrophysiological experimental data. Recently, it has been provedthat signal transmission and neuronal energetic demands are tightly coupledto information coding in the cerebral cortex in functional magneticresonance imaging (fMRI) experiments. Therefore, the analytic resultsobtained in this paper show that the principle of energy coding is quitefundamental and is beneficial to the study of the important scientificproblem as how the brain performs coding at the level of local neuralnetworks.

     

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