Evaluating automated parameter constraining procedures of neuron models by experimental and surrogate data
Evaluating automated parameter constraining procedures of neuron models by experimental and surrogate data
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DOI：
10.1007/s00422-008-0269-2
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发表时间：
2008-11-01
影响因子：
1.9
通讯作者：
Segev, Idan
中科院分区：
文献类型：
--
作者：
Druckmann, Shaul;Berger, Thomas K.;Segev, Idan
神经元模型，特别是基于电导的区室模型，通常具有许多无法直接通过实验确定的参数，并且必须受到优化程序的约束。评估此类程序的实用性的常见做法是使用先前开发的模型来生成替代数据（例如，阶跃电流脉冲之后的尖峰痕迹），然后挑战算法以恢复用于生成数据的原始参数（例如，最大离子通道电导值）。以这种方式，可以容易地确定寻找原始参数的模型拟合过程的成功或失败。在这里，我们展示了一些在模型与模型比较的情况下提供出色拟合的模型拟合程序，当应用于实验数据时却提供了不平衡的结果。主要原因是替代数据和实验数据测试了算法功能的不同方面。当考虑模型生成的代理数据时，算法需要找到已知存在的完美解决方案。相反，当考虑实验目标数据时，不能保证完美的解决方案是搜索空间的一部分。在这种情况下，优化过程必须对所有不完美的近似进行排序，并最终选择最佳的近似。在考虑替代数据时，根本没有测试这一方面，因为已知至少存在一个完美的解决方案（原始参数），使得所有近似都是不必要的。此外，我们证明，基于从目标数据中提取一组特征（例如首次尖峰时间、尖峰宽度、尖峰频率等）的距离函数，而不是使用原始数据（例如，整个尖峰轨迹）作为拟合目标，能够找到与实验数据很好近似的不完美解决方案。
Neuron models, in particular conductance-based compartmental models, often have numerous parameters that cannot be directly determined experimentally and must be constrained by an optimization procedure. A common practice in evaluating the utility of such procedures is using a previously developed model to generate surrogate data (e.g., traces of spikes following step current pulses) and then challenging the algorithm to recover the original parameters (e.g., the value of maximal ion channel conductances) that were used to generate the data. In this fashion, the success or failure of the model fitting procedure to find the original parameters can be easily determined. Here we show that some model fitting procedures that provide an excellent fit in the case of such model-to-model comparisons provide ill-balanced results when applied to experimental data. The main reason is that surrogate and experimental data test different aspects of the algorithm's function. When considering model-generated surrogate data, the algorithm is required to locate a perfect solution that is known to exist. In contrast, when considering experimental target data, there is no guarantee that a perfect solution is part of the search space. In this case, the optimization procedure must rank all imperfect approximations and ultimately select the best approximation. This aspect is not tested at all when considering surrogate data since at least one perfect solution is known to exist (the original parameters) making all approximations unnecessary. Furthermore, we demonstrate that distance functions based on extracting a set of features from the target data (such as time-to-first-spike, spike width, spike frequency, etc.)-rather than using the original data (e.g., the whole spike trace) as the target for fitting-are capable of finding imperfect solutions that are good approximations of the experimental data.