Optimal Current Transfer in Dendrites.
Optimal Current Transfer in Dendrites.
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DOI：
10.1371/journal.pcbi.1004897
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发表时间：
2016-05
影响因子：
4.3
通讯作者：
Cuntz H
中科院分区：
文献类型：
--
作者：
Bird AD;Cuntz H
在庞大的树突树上整合突触电流是大脑进行计算的先决条件。树突从体细胞逐渐变细被认为可以平衡来自不同位置的突触的贡献，并最大限度地将电流转移到体细胞。为了弄清楚这是如何精确实现的，需要对具有任意锥度的枝晶中的电流转移给出解析解。我们在此推导出一个与数值模拟结果精确匹配的渐近近似。从这个我们然后确定直径轮廓，最大限度地将电流转移到体细胞。我们发现了一个简单的二次型，与实验得到的直径相匹配，这表明大脑的基本结构原理将树突直径与信号传输联系起来。神经元的形状千差万别，使它们能够在大脑中执行不同的计算角色。许多神经元最显著的可见特征是，它们的树突树是由索状突起组成的广泛分支网络。神经元从树突树上的其他细胞接收电流诱导的突触接触。就像植物树的情况一样，树突树向其尖端明显变细。与恒定宽度相比，这种逐渐变细的方式有许多优点，既可以减少能量需求，也可以在不同位置整合输入。然而，为了预测神经元执行的计算，输入电流的解析解倾向于假设恒定的树突直径。在这里，我们引入了一个渐进逼近，准确地模拟了具有任意，连续变化的直径的树突树的电流传递。当我们确定最大限度地向细胞体转移电流的直径分布图时，我们发现直径与在真实神经元中观察到的直径相似。我们的结论是，树突树的逐渐变细以优化信号传输是大脑的基本结构原则。
Integration of synaptic currents across an extensive dendritic tree is a prerequisite for computation in the brain. Dendritic tapering away from the soma has been suggested to both equalise contributions from synapses at different locations and maximise the current transfer to the soma. To find out how this is achieved precisely, an analytical solution for the current transfer in dendrites with arbitrary taper is required. We derive here an asymptotic approximation that accurately matches results from numerical simulations. From this we then determine the diameter profile that maximises the current transfer to the soma. We find a simple quadratic form that matches diameters obtained experimentally, indicating a fundamental architectural principle of the brain that links dendritic diameters to signal transmission. Neurons take a great variety of shapes that allow them to perform their different computational roles across the brain. The most distinctive visible feature of many neurons is the extensively branched network of cable-like projections that make up their dendritic tree. A neuron receives current-inducing synaptic contacts from other cells across its dendritic tree. As in the case of botanical trees, dendritic trees are strongly tapered towards their tips. This tapering has previously been shown to offer a number of advantages over a constant width, both in terms of reduced energy requirements and the robust integration of inputs at different locations. However, in order to predict the computations that neurons perform, analytical solutions for the flow of input currents tend to assume constant dendritic diameters. Here we introduce an asymptotic approximation that accurately models the current transfer in dendritic trees with arbitrary, continuously changing, diameters. When we then determine the diameter profiles that maximise current transfer towards the cell body we find diameters similar to those observed in real neurons. We conclude that the tapering in dendritic trees to optimise signal transmission is a fundamental architectural principle of the brain.