Rudolph M, Destexhe A
Unité de Neuroscience Intégratives et Computationnelles, CNRS, 91198 Gif-sur-Yvette, France.
Neural Comput. 2005 Nov;17(11):2301-15. doi: 10.1162/0899766054796932.
Synaptically generated subthreshold membrane potential (Vm) fluctuations can be characterized within the framework of stochastic calculus. It is possible to obtain analytic expressions for the steady-state Vm distribution, even in the case of conductance-based synaptic currents. However, as we show here, the analytic expressions obtained may substantially deviate from numerical solutions if the stochastic membrane equations are solved exclusively based on expectation values of differentials of the stochastic variables, hence neglecting the spectral properties of the underlying stochastic processes. We suggest a simple solution that corrects these deviations, leading to extended analytic expressions of the Vm distribution valid for a parameter regime that covers several orders of magnitude around physiologically realistic values. These extended expressions should enable finer characterization of the stochasticity of synaptic currents by analyzing experimentally recorded Vm distributions and may be applicable to other classes of stochastic processes as well.
通过随机微积分框架可以对突触产生的阈下膜电位(Vm)波动进行表征。即使对于基于电导的突触电流,也有可能获得稳态Vm分布的解析表达式。然而,正如我们在此处所示,如果仅基于随机变量微分的期望值来求解随机膜方程,从而忽略潜在随机过程的频谱特性,那么所得到的解析表达式可能会与数值解有很大偏差。我们提出了一种简单的解决方案来校正这些偏差,从而得到Vm分布的扩展解析表达式,该表达式在围绕生理现实值的几个数量级的参数范围内都是有效的。这些扩展表达式应该能够通过分析实验记录的Vm分布来更精细地表征突触电流的随机性,并且可能也适用于其他类别的随机过程。
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