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首页> 外文期刊>Journal of Computational Neuroscience >Automated evolutionary optimization of ion channel conductances and kinetics in models of young and aged rhesus monkey pyramidal neurons
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Automated evolutionary optimization of ion channel conductances and kinetics in models of young and aged rhesus monkey pyramidal neurons

机译：幼小和老年恒河猴锥体神经元模型中离子通道电导和动力学的自动进化优化

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摘要

Conductance-based compartment modeling requires tuning of many parameters to fit the neuron model to target electrophysiological data. Automated parameter optimization via evolutionary algorithms (EAs) is a common approach to accomplish this task, using error functions to quantify differences between model and target. We present a three-stage EA optimization protocol for tuning ion channel conductances and kinetics in a generic neuron model with minimal manual intervention. We use the technique of Latin hypercube sampling in a new way, to choose weights for error functions automatically so that each function influences the parameter search to a similar degree. This protocol requires no specialized physiological data collection and is applicable to commonly-collected current clamp data and either single- or multi-objective optimization. We applied the protocol to two representative pyramidal neurons from layer 3 of the prefrontal cortex of rhesus monkeys, in which action potential firing rates are significantly higher in aged compared to young animals. Using an idealized dendritic topology and models with either 4 or 8 ion channels (10 or 23 free parameters respectively), we produced populations of parameter combinations fitting the target datasets in less than 80 hours of optimization each. Passive parameter differences between young and aged models were consistent with our prior results using simpler models and hand tuning. We analyzed parameter values among fits to a single neuron to facilitate refinement of the underlying model, and across fits to multiple neurons to show how our protocol will lead to predictions of parameter differences with aging in these neurons.
机译：基于电导的隔室建模需要调整许多参数，以使神经元模型适合目标电生理数据。通过进化算法（EA）进行自动参数优化是完成此任务的常用方法，它使用误差函数来量化模型与目标之间的差异。我们提出了一个三阶段的EA优化协议，用于以最少的人工干预来调整通用神经元模型中的离子通道电导和动力学。我们以一种新的方式使用拉丁超立方体采样技术，为误差函数自动选择权重，以便每个函数在相似程度上影响参数搜索。该协议不需要专门的生理数据收集，并且适用于通常收集的当前钳位数据以及单目标或多目标优化。我们将该协议应用于恒河猴前额叶皮层第3层的两个代表性锥体神经元，其中年龄较年轻动物的动作电位放电速率明显更高。使用理想的树状拓扑和具有4个或8个离子通道（分别有10个或23个自由参数）的模型，我们在不到80小时的优化时间内就生成了适合目标数据集的参数组合。年轻人和老年人模型之间的被动参数差异与我们先前使用较简单模型和手动调整的结果一致。我们分析了单个神经元的拟合之间的参数值，以促进基础模型的完善，并跨多个神经元的拟合进行分析，以显示我们的协议将如何预测这些神经元的衰老参数差异。

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