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首页> 外文期刊>Journal of Neuroscience Methods >Errors in the estimation of the variance: Implications for multiple-probability fluctuation analysis.
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Errors in the estimation of the variance: Implications for multiple-probability fluctuation analysis.

机译:方差估计中的错误:多概率波动分析的含义。

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摘要

Synapses play a crucial role in information processing in the brain. Amplitude fluctuations of synaptic responses can be used to extract information about the mechanisms underlying synaptic transmission and its modulation. In particular, multiple-probability fluctuation analysis can be used to estimate the number of functional release sites, the mean probability of release and the amplitude of the mean quantal response from fits of the relationship between the variance and mean amplitude of postsynaptic responses, recorded at different probabilities. To determine these quantal parameters, calculate their uncertainties and the goodness-of-fit of the model, it is important to weight the contribution of each data point in the fitting procedure. We therefore investigated the errors associated with measuring the variance by determining the best estimators of the variance of the variance and have used simulations of synaptic transmission to test their accuracy and reliability under different experimental conditions. For central synapses, which generally have a low number of release sites, the amplitude distribution of synaptic responses is not normal, thus the use of a theoretical variance of the variance based on the normal assumption is not a good approximation. However, appropriate estimators can be derived for the population and for limited sample sizes using a more general expression that involves higher moments and introducing unbiased estimators based on the h-statistics. Our results are likely to be relevant for various applications of fluctuation analysis when few channels or release sites are present.
机译:突触在大脑的信息处理中起着至关重要的作用。突触反应的幅度波动可用于提取有关突触传递及其调控机制的信息。特别是,多概率波动分析可用于估计功能释放位点的数量,平均释放概率和平均数量反应的振幅,其根据突触后反应的方差和平均振幅之间的关系进行拟合,并记录在不同的概率。为了确定这些量化参数,计算其不确定性和模型的拟合优度,重要的是在拟合过程中权衡每个数据点的贡献。因此,我们通过确定方差方差的最佳估计量来调查与测量方差相关的误差,并使用突触传递的模拟来测试其在不同实验条件下的准确性和可靠性。对于通常具有少量释放位点的中央突触,突触响应的幅度分布不正常,因此基于正常假设使用方差的理论方差不是一个很好的近似值。但是,可以使用更复杂的表达式(包含更高的矩)并基于h统计量引入无偏估计量,来为总体和有限样本量导出适当的估计量。当通道或释放位置很少时,我们的结果可能与波动分析的各种应用有关。

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