Estimating Time-Varying Applied Current in the Hodgkin-Huxley Model
Estimating Time-Varying Applied Current in the Hodgkin-Huxley Model
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DOI：
10.3390/app10020550
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发表时间：
2019-11
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影响因子：
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通讯作者：
Kayleigh S J Campbell;Laura Staugler;Andrea Arnold
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文献类型：
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作者：
Kayleigh S J Campbell;Laura Staugler;Andrea Arnold
经典的Hodgkin-Huxley模型被广泛用于理解单个神经元的电生理动力学。虽然向系统施加低幅度恒定电流会导致单个电压尖峰，但通过施加时变电流可能会产生多个电压尖峰，这可能无法通过实验测量。这项工作的目的是估计不同的确定性形式的噪声电压数据随时间变化的应用电流。特别是，我们利用具有参数跟踪的增强系综卡尔曼滤波器来估计四个不同的时变应用电流参数和相关的霍奇金-赫胥黎模型状态，以及每种情况下的不确定性界限，沿着。我们测试的效率参数跟踪算法在此设置中，通过分析的影响，改变标准偏差的参数漂移和频率的数据上得到的随时间变化的应用当前的估计和相关的不确定性。
The classic Hodgkin-Huxley model is widely used for understanding the electrophysiological dynamics of a single neuron. While applying a low-amplitude constant current to the system results in a single voltage spike, it is possible to produce multiple voltage spikes by applying time-varying currents, which may not be experimentally measurable. The aim of this work is to estimate time-varying applied currents of different deterministic forms given noisy voltage data. In particular, we utilize an augmented ensemble Kalman filter with parameter tracking to estimate four different time-varying applied current parameters and associated Hodgkin-Huxley model states, along with uncertainty bounds in each case. We test the efficiency of the parameter tracking algorithm in this setting by analyzing the effects of changing the standard deviation of the parameter drift and the frequency of data available on the resulting time-varying applied current estimates and related uncertainty.