统计数据同化的HVC中间神经元特性
HVC Interneuron Properties from Statistical Data Assimilation
摘要：
数据同化（DA）解决的是在给定数据和模式的情况下推断初始条件的逆问题。在这里，我们使用生物动力学的Hodgkin-Huxley（HH）模型的鸟类HVCI神经元，实验获得的记录，这些神经元，和我们的数据同化算法来推断完整的参数集和一组最小的离子电流精确地再现所观察到的波形信息。我们发现我们的DA方法和模型的选择，选择了许多不同的验证参数集。我们的结论是探索应用于神经元产生准确或不准确的结果的逆问题的变化;通过操纵数据的算法，不同的采样率和波形;并通过添加和减去离子电流操纵模型。
英文摘要：
Data assimilation (DA) solves the inverse problem of inferring initial conditions given data and a model. Here we use biophysically motivated Hodgkin-Huxley (HH) models of avian HVCI neurons, experimentally obtained recordings of these neurons, and our data assimilation algorithm to infer the full set of parameters and a minimal set of ionic currents precisely reproducing the observed waveform information. We find many distinct validated sets of parameters selected by our DA method and choice of model. We conclude exploring variations on the inverse problem applied to neurons producing accurate or inaccurate results; by manipulating data presented to the algorithm, varying sample rate and waveform; and by manipulating the model by adding and subtracting ionic currents.
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