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Power Laws from Linear Neuronal Cable Theory: Power Spectral Densities of the Soma Potential Soma Membrane Current and Single-Neuron Contribution to the EEG

机译:线性神经元电缆理论的功率定律:Soma势的功率谱密度Soma膜电流和对EEG的单神经元贡献

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摘要

Power laws, that is, power spectral densities (PSDs) exhibiting behavior for large frequencies f, have been observed both in microscopic (neural membrane potentials and currents) and macroscopic (electroencephalography; EEG) recordings. While complex network behavior has been suggested to be at the root of this phenomenon, we here demonstrate a possible origin of such power laws in the biophysical properties of single neurons described by the standard cable equation. Taking advantage of the analytical tractability of the so called ball and stick neuron model, we derive general expressions for the PSD transfer functions for a set of measures of neuronal activity: the soma membrane current, the current-dipole moment (corresponding to the single-neuron EEG contribution), and the soma membrane potential. These PSD transfer functions relate the PSDs of the respective measurements to the PSDs of the noisy input currents. With homogeneously distributed input currents across the neuronal membrane we find that all PSD transfer functions express asymptotic high-frequency power laws with power-law exponents analytically identified as for the soma membrane current, for the current-dipole moment, and for the soma membrane potential. Comparison with available data suggests that the apparent power laws observed in the high-frequency end of the PSD spectra may stem from uncorrelated current sources which are homogeneously distributed across the neural membranes and themselves exhibit pink () noise distributions. While the PSD noise spectra at low frequencies may be dominated by synaptic noise, our findings suggest that the high-frequency power laws may originate in noise from intrinsic ion channels. The significance of this finding goes beyond neuroscience as it demonstrates how power laws with a wide range of values for the power-law exponent α may arise from a simple, linear partial differential equation.
机译:在微观(神经膜电位和电流)和宏观(脑电图; EEG)记录中都观察到了幂定律,即表现出大频率f行为的功率谱密度(PSD)。虽然复杂的网络行为被认为是这种现象的根源,但我们在这里证明了由标准电缆方程描述的单个神经元的生物物理特性中此类幂律的可能起源。利用所谓的球和棒神经元模型的分析易处理性,我们导出了PSD传递函数的一般表达式,用于一系列神经元活动量度:体膜电流,电流偶极矩(对应于单神经元脑电图的贡献)和体膜电位。这些PSD传递函数将各个测量的PSD与噪声输入电流的PSD关联起来。通过在整个神经元膜上均匀分布的输入电流,我们发现所有PSD传递函数均表现出渐近的高频幂定律,且幂律指数经分析确定为体膜电流,电流偶极矩和体膜电位。与可用数据的比较表明,在PSD频谱的高频端观察到的表观功率定律可能源于不相关的电流源,这些电流源均匀地分布在整个神经膜上,并且自身呈现出粉红色()噪声分布。虽然低频处的PSD噪声频谱可能由突​​触噪声主导,但我们的发现表明,高频功率定律可能源自固有离子通道的噪声。这一发现的意义超出了神经科学领域,因为它证明了幂律指数α的取值范围较广的幂律如何从简单的线性偏微分方程中产生。

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