Exact solutions to cable equations in branching neurons with tapering dendrites.

IF 2.3 4区 医学 Q1 Neuroscience
Lu Yihe, Yulia Timofeeva
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引用次数: 0

Abstract

Neurons are biological cells with uniquely complex dendritic morphologies that are not present in other cell types. Electrical signals in a neuron with branching dendrites can be studied by cable theory which provides a general mathematical modelling framework of spatio-temporal voltage dynamics. Typically such models need to be solved numerically unless the cell membrane is modelled either by passive or quasi-active dynamics, in which cases analytical solutions can be reduced to calculation of the Green's function describing the fundamental input-output relationship in a given morphology. Such analytically tractable models often assume individual dendritic segments to be cylinders. However, it is known that dendritic segments in many types of neurons taper, i.e. their radii decline from proximal to distal ends. Here we consider a generalised form of cable theory which takes into account both branching and tapering structures of dendritic trees. We demonstrate that analytical solutions can be found in compact algebraic forms in an arbitrary branching neuron with a class of tapering dendrites studied earlier in the context of single neuronal cables by Poznanski (Bull. Math. Biol. 53(3):457-467, 1991). We apply this extended framework to a number of simplified neuronal models and contrast their output dynamics in the presence of tapering versus cylindrical segments.

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具有锥形树突的分支神经元索方程的精确解。
神经元是一种生物细胞,具有其他细胞类型所没有的独特复杂的树突形态。具有分支树突的神经元中的电信号可通过电缆理论进行研究,该理论为时空电压动态提供了一个通用的数学建模框架。通常情况下,此类模型需要进行数值求解,除非细胞膜采用被动或准主动动力学建模,在这种情况下,分析求解可简化为计算描述给定形态中基本输入-输出关系的格林函数。这种可分析的模型通常假定单个树突节段为圆柱体。然而,众所周知,许多类型的神经元的树突节段都是锥形的,即它们的半径从近端向远端递减。在此,我们将考虑树突树的分枝和渐细结构,研究索状理论的一般形式。我们证明,在波兹南斯基(Bull. Math. Poznanski)早先研究的单神经元缆索的背景下,可以用紧凑的代数形式找到任意分支神经元与一类锥形树突的解析解(Bull.Math.53(3):457-467, 1991)。我们将这一扩展框架应用于一些简化的神经元模型,并对比了它们在锥形和圆柱形神经节存在时的输出动态。
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来源期刊
Journal of Mathematical Neuroscience
Journal of Mathematical Neuroscience Neuroscience-Neuroscience (miscellaneous)
自引率
0.00%
发文量
0
审稿时长
13 weeks
期刊介绍: The Journal of Mathematical Neuroscience (JMN) publishes research articles on the mathematical modeling and analysis of all areas of neuroscience, i.e., the study of the nervous system and its dysfunctions. The focus is on using mathematics as the primary tool for elucidating the fundamental mechanisms responsible for experimentally observed behaviours in neuroscience at all relevant scales, from the molecular world to that of cognition. The aim is to publish work that uses advanced mathematical techniques to illuminate these questions. It publishes full length original papers, rapid communications and review articles. Papers that combine theoretical results supported by convincing numerical experiments are especially encouraged. Papers that introduce and help develop those new pieces of mathematical theory which are likely to be relevant to future studies of the nervous system in general and the human brain in particular are also welcome.
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