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用房室模型估计神经元的电紧张结构。

Estimating the electrotonic structure of neurons with compartmental models.

作者信息

Holmes W R, Rall W

机构信息

Mathematical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland 20892.

出版信息

J Neurophysiol. 1992 Oct;68(4):1438-52. doi: 10.1152/jn.1992.68.4.1438.

Abstract
  1. A procedure based on compartmental modeling called the "constrained inverse computation" was developed for estimating the electrotonic structure of neurons. With the constrained inverse computation, a set of N electrotonic parameters are estimated iteratively with use of a Newton-Raphson algorithm given values of N parameters that can be measured or estimated from experimental data. 2. The constrained inverse computation is illustrated by several applications to the basic example of a neuron represented as one cylinder coupled to a soma. The number of unknown parameters estimated was different (ranging from 2 to 6) when different sets of constraints were chosen. The unknowns were chosen from the following: dendritic membrane resistivity Rmd, soma membrane resistivity Rms, intracellular resistivity Ri, membrane capacity Cm, dendritic membrane area AD, soma membrane area As, electrotonic length L, and resistivity-free length, rfl (rfl = 2l/d1/2 where l and d are length and diameter of the cylinder). The values of the unknown parameters were estimated from the values of an equal number of known parameters, which were chosen from the following: the time constants and coefficients of a voltage transient tau 0, tau 1, ..., C0, C1, ..., voltage-clamp time constants tau vc1, tau vc2, ..., and input resistance RN. Note that initially, morphological data were treated as unknown, rather than known. 3. When complete morphology was not known, parameters from voltage and current transients, combined with the input resistance were not sufficient to completely specify the electrotonic structure of the neuron. For a neuron represented as a cylinder coupled to a soma, there were an infinite number of combinations of Rmd, Rms, Ri, Cm, AS, AD, and L that could be fitted to the same voltage and current transients and input resistance. 4. One reason for the nonuniqueness when complete morphology was not specified is that the Ri estimate is intrinsically bound to the morphology. Ri enters the inverse computation only in the calculation of the electrotonic length of a compartment. The electrotonic length of a compartment is l[4 Ri/(dRmd)]1/2, where l and d are the length and diameter of the compartment. Without complete morphology, the inverse computation cannot distinguish between a change in d or l and a change in Ri. Even when morphology is known, the accuracy of the Ri estimate obtained by any fitting procedure is affected by systematic errors in length and diameter measurements (i.e., tissue shrinkage); the Ri estimate is inversely proportional to the length measurement and proportional to the square root of the diameter measurement.(ABSTRACT TRUNCATED AT 400 WORDS)
摘要
  1. 开发了一种基于房室模型的程序,称为“约束逆计算”,用于估计神经元的电紧张结构。通过约束逆计算,利用牛顿-拉夫逊算法,在给定可从实验数据测量或估计的N个参数值的情况下,迭代估计一组N个电紧张参数。2. 通过对表示为与胞体相连的一个圆柱体的神经元基本示例的几个应用,来说明约束逆计算。当选择不同的约束集时,估计的未知参数数量不同(范围从2到6)。未知参数从以下各项中选择:树突膜电阻率Rmd、胞体膜电阻率Rms、细胞内电阻率Ri、膜电容Cm、树突膜面积AD、胞体膜面积As、电紧张长度L和无电阻率长度rfl(rfl = 2l/d1/2,其中l和d是圆柱体的长度和直径)。未知参数的值从数量相等的已知参数值中估计,这些已知参数从以下各项中选择:电压瞬变tau 0、tau 1、...、C0、C1、...的时间常数和系数、电压钳制时间常数tau vc1、tau vc2、...以及输入电阻RN。请注意,最初,形态学数据被视为未知,而非已知。3. 当完整的形态学未知时,来自电压和电流瞬变的参数与输入电阻相结合,不足以完全确定神经元的电紧张结构。对于表示为与胞体相连的圆柱体的神经元,存在无数种Rmd、Rms、Ri、Cm、AS、AD和L的组合,可以拟合到相同的电压和电流瞬变以及输入电阻。4. 当未指定完整形态学时出现非唯一性的一个原因是,Ri估计本质上与形态学相关。Ri仅在计算房室的电紧张长度时进入逆计算。房室的电紧张长度为l[4 Ri/(dRmd)]1/2,其中l和d是房室的长度和直径。没有完整的形态学,逆计算无法区分d或l的变化与Ri的变化。即使形态学已知,通过任何拟合程序获得的Ri估计的准确性也会受到长度和直径测量中的系统误差(即组织收缩)的影响;Ri估计与长度测量成反比,与直径测量的平方根成正比。(摘要截断于400字)

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