A flexible, interactive software tool for fitting the parameters of neuronal models.
A flexible, interactive software tool for fitting the parameters of neuronal models.
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DOI：
10.3389/fninf.2014.00063
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发表时间：
2014
影响因子：
3.5
通讯作者：
Káli S
中科院分区：
文献类型：
--
作者：
Friedrich P;Vella M;Gulyás AI;Freund TF;Káli S
生物相关神经元模型的构建以及基于模型的实验数据分析通常需要同时拟合多个模型参数，以便模型在某种范式中的行为根据某些预定义的标准与真实神经元的相应输出匹配（尽可能接近）。尽管模型优化的任务通常在计算上很困难，并且结果的质量在很大程度上取决于技术问题，例如成本函数和优化算法的适当选择（和实现），但现有的程序在提供最佳可用方法的同时还无法有效地指导用户完成整个过程。我们的软件称为 Optimizer，实现了用于神经元模型优化的模块化和可扩展框架，并且还具有图形界面，即使是非专家用户也可以轻松处理许多常见情况。同时，受过教育的用户可以扩展程序的功能并根据自己的需要进行定制，而花费的精力相对较少。 Optimizer 使用 Python 开发，利用开源 Python 模块进行非线性优化，并直接与 NEURON 模拟器连接来运行模型。其他模拟器通过外部接口支持。我们使用不同的模型类针对不同复杂程度的几种不同类型的问题测试了该程序。作为目标，我们使用来自相同或更复杂模型类的模拟轨迹以及实验数据。我们成功地使用 Optimizer 确定了隔室模型中的被动参数和电导密度，并将简单（自适应指数积分和激发）神经元模型拟合到复杂的生物数据。我们的详细比较表明，Optimizer 可以处理更广泛的问题，并且提供与任何其他现有神经元模型拟合工具同样好的或更好的性能。
The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool.
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