Dissecting estimation of conductances in subthreshold regimes
Dissecting estimation of conductances in subthreshold regimes
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DOI：
10.1007/s10827-015-0576-2
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发表时间：
2015-12-01
影响因子：
1.2
通讯作者：
Guillamon, Antoni
中科院分区：
文献类型：
--
作者：
Vich, Catalina;Guillamon, Antoni
我们研究了阈下活动的影响，在突触电导的估计。众所周知，在加标方案中，实际电导与使用线性回归方法估计的电导之间的差异可能很大，因此在进行线性估计之前，应注意从实验数据中去除加标活性。然而，并没有太多的注意力已经支付的影响，离子电流活跃在非尖峰制度，这种线性方法仍然大量使用。在本文中，我们使用基于电导的模型来测试这种影响，并使用几种代表性机制来诱导离子亚阈值活动。在所有的情况下，我们表明，在阈下活动激活的电流可以导致显着的错误时，估计突触电导线性。因此，我们的研究结果增加了一个新的警告信息时，从细胞内记录和结论，可以从中得出有关神经元活动的电导痕迹。此外，我们提出，作为概念的证明，一种替代方法，考虑到特定的离子亚阈值电流的主要非线性效应。这种方法，基于亚阈值动力学的平方化，使我们能够减少一个数量级以上的估计电导的相对误差。在实验条件下，在适当拟合正则模型的情况下，即使在存在噪声的情况下也可以获得更好的估计。
We study the influence of subthreshold activity in the estimation of synaptic conductances. It is known that differences between actual conductances and the estimated ones using linear regression methods can be huge in spiking regimes, so caution has been taken to remove spiking activity from experimental data before proceeding to linear estimation. However, not much attention has been paid to the influence of ionic currents active in the non-spiking regime where such linear methods are still profusely used. In this paper, we use conductance-based models to test this influence using several representative mechanisms to induce ionic subthreshold activity. In all the cases, we show that the currents activated during subthreshold activity can lead to significant errors when estimating synaptic conductance linearly. Thus, our results add a new warning message when extracting conductance traces from intracellular recordings and the conclusions concerning neuronal activity that can be drawn from them. Additionally, we present, as a proof of concept, an alternative method that takes into account the main nonlinear effects of specific ionic subthreshold currents. This method, based on the quadratization of the subthreshold dynamics, allows us to reduce the relative errors of the estimated conductances by more than one order of magnitude. In experimental conditions, under appropriate fitting to canonical models, it could be useful to obtain better estimations as well even under the presence of noise.