A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data.
A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data.
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DOI：
10.3389/neuro.01.1.1.001.2007
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发表时间：
2007-11
影响因子：
4.3
通讯作者：
Segev, Idan
中科院分区：
文献类型：
--
作者：
Druckmann, Shaul;Banitt, Yoav;Gidon, Albert;Schurmann, Felix;Markram, Henry;Segev, Idan
关键词：
我们提出了一个新的框架来自动约束神经元的室室模型参数，给出了大量的实验测量这些神经元的反应。在实验中，对完全相同的输入进行重复的电压响应中，固有噪声会引起很大的可变性（例如，在发射模式中）。因此，拟合模型的常用方法是试图一点一点地完美复制，从变量响应的光谱中选择一个单一的轨迹，这似乎对数据不公正。此外，找到一个忠实地描述两个尖峰迹线之间距离的单一误差函数并不是一件容易的事情。为了解决这些问题，可以采用允许联合使用几个误差函数的多目标优化方法。当有多个误差函数可用时，可以根据感兴趣的单个特征（例如，尖峰速率，尖峰宽度）进行实验电压走线和模型响应之间的比较。每个特征可以在模型和实验平均值之间进行比较，以其实验变异性为单位，从而将这种变异性纳入拟合。我们证明了这种方法的成功，当与遗传算法优化结合使用时，在模型行为和两种不同的皮层中间神经元的放电模式之间产生了极好的拟合，适应和快速尖峰。我们认为，由这种方法生成的多种多样的模型可以作为大型神经网络现实模拟的构建块。
We present a novel framework for automatically constraining parameters of compartmental models of neurons, given a large set of experimentally measured responses of these neurons. In experiments, intrinsic noise gives rise to a large variability (e.g., in firing pattern) in the voltage responses to repetitions of the exact same input. Thus, the common approach of fitting models by attempting to perfectly replicate, point by point, a single chosen trace out of the spectrum of variable responses does not seem to do justice to the data. In addition, finding a single error function that faithfully characterizes the distance between two spiking traces is not a trivial pursuit. To address these issues, one can adopt a multiple objective optimization approach that allows the use of several error functions jointly. When more than one error function is available, the comparison between experimental voltage traces and model response can be performed on the basis of individual features of interest (e.g., spike rate, spike width). Each feature can be compared between model and experimental mean, in units of its experimental variability, thereby incorporating into the fitting this variability. We demonstrate the success of this approach, when used in conjunction with genetic algorithm optimization, in generating an excellent fit between model behavior and the firing pattern of two distinct electrical classes of cortical interneurons, accommodating and fast-spiking. We argue that the multiple, diverse models generated by this method could serve as the building blocks for the realistic simulation of large neuronal networks.
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影响因子：
5.5
作者：
Korngreen, A;Sakmann, B
通讯作者：
Sakmann, B
影响因子：
5.5
作者：
Bekkers, JM
通讯作者：
Bekkers, JM
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Kole, MHP;Hallermann, S;Stuart, GJ
通讯作者：
Stuart, GJ
影响因子：
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作者：
Bibbig, A;Faulkner, HJ;Traub, RD
通讯作者：
Traub, RD
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作者：
Golowasch, J;Goldman, MS;Marder, E
通讯作者：
Marder, E