Active dendrites reduce location-dependent variability of synaptic input trains
Active dendrites reduce location-dependent variability of synaptic input trains
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DOI：
10.1152/jn.1997.78.4.2116
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发表时间：
1997-10-01
影响因子：
2.5
通讯作者：
Johnston, D
中科院分区：
文献类型：
--
作者：
Cook, EP;Johnston, D
我们研究了树突状电压门控通道可以减少突触位置对短突触序列（称为位置依赖性变异）模式的体细胞去极化的影响的假设。三个计算机模型的重建海马CAI细胞，每一个增加现实主义和复杂性，被使用。对于每个模型，目标是确定最能降低位置依赖性变异性的树突组成。第一个模型是线性的，单个参数，树突状细胞膜电导（G（Dm），其中R-m = 1/G（Dm）），是变化的。令人惊讶的是，负G（Dm）最小化了位置相关的变异性。叠加的突触输入表明，与被动树突相比，主动树突增加的个人反应的平均值，同时减少在不同位置的突触之间的方差。主动树突补偿随离索马的距离增加而增加的被动电缆信号干扰的三个组成部分：树突膜电容上的电荷积累、跨越突触和非突触树突膜电导的电荷逃逸以及由于离索马较远的树突的去极化增加而导致的突触电荷进入的减少。我们还发现，整个活跃的树突树贡献电荷给任何一个活跃的突触。第二个模型包含一个人工的电压依赖性电流（I-boost）添加到被动的顶端树突。最佳量的I-boost，最大限度地减少位置依赖性的变化被发现是独立的强度的个别突触输入，但成反比的突触持续时间。在第三个模型中，现实的T型Ca 2+和持久性Na+通道模型被添加到被动树突和数值拟合再现的影响，I-升压。两种现实电流都最大限度地减少了突触变异性。真实树突电流的密度并不均匀，但显示出细微的变化，并随着与索马的距离略有减少。一个异联想记忆网络也被建模，以证明位置依赖的变异性和记忆回忆性能之间的重要关系。与被动树突相比，主动树突通过减少回忆错误来增加记忆储存。这些模拟表明，主动树突可以最小化被动树突的电缆属性，并增强索马的能力，以确定突触输入的强度。这些模型预测，最小化位置依赖性变化的树突将具有精确调谐的整体负斜率电导I-V关系。
We examined the hypothesis that dendritic voltage-gated channels can reduce the effect synaptic location has on somatic depolarization in response to patterns of short synaptic trains (referred to as location-dependent variability). Three computer models of a reconstructed hippocampal CAI cell, each of increasing realism and complexity, were used. For each model, the goal was to identify the dendritic composition that best reduced the location-dependent variability. The first model was linear and a single parameter, dendritic membrane conductance (G(Dm), where R-m = 1/G(Dm)), was varied. Surprisingly, a negative G(Dm) minimized the location-dependent variability. Superposition of the synaptic inputs showed that, compared with passive dendrites, active dendrites increase the mean of the individual responses while decreasing the variance between synapses at different locations. Active dendrites compensate the three components of passive cable signal interference that increase with distance from the soma: the accumulation of charge on dendritic membrane capacitance, the escape of charge across synaptic and nonsynaptic dendritic membrane conductances, and the reduction in synaptic charge entry due to increased depolarization of dendrites located farther from the soma. We also found that the entire active dendritic tree contributes charge to any one active synapse. The second model contained an artificial voltage-dependent current (I-boost) added to passive apical dendrites. The optimal amount of I-boost that minimized location-dependent variability was found to be independent of the strength of individual synaptic inputs but inversely related to the synaptic duration. In the third model, realistic T-type Ca2+ and persistent Na+ channel models were added to passive dendrites and numerically fit to reproduce the effects of I-boost. Both realistic currents minimized synaptic variability. The densities for the realistic dendritic currents were not uniform but showed subtle variations and a slight reduction with distance from the soma. A heteroassociative memory network also was modeled to demonstrate the important relationship between location-dependent variability and memory recall performance. Compared with passive dendrites, active dendrites increased memory storage by reducing recall errors. These simulations demonstrate that active dendrites can minimize the cable properties of passive dendrites and enhance the soma's ability to determine the strength of the synaptic input. These models predict dendrites that minimize location-dependent variability will have an overall negative slope conductance I-V relationship that is tuned precisely.