Parameter Optimization Using Covariance Matrix Adaptation-Evolutionary Strategy (CMA-ES), an Approach to Investigate Differences in Channel Properties Between Neuron Subtypes.
Parameter Optimization Using Covariance Matrix Adaptation-Evolutionary Strategy (CMA-ES), an Approach to Investigate Differences in Channel Properties Between Neuron Subtypes.
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DOI：
10.3389/fninf.2018.00047
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发表时间：
2018
影响因子：
3.5
通讯作者：
Blackwell KT
中科院分区：
文献类型：
--
作者：
Jȩdrzejewski-Szmek Z;Abrahao KP;Jȩdrzejewska-Szmek J;Lovinger DM;Blackwell KT
神经科学中的计算模型可用于预测神经元和网络中的生物机制之间的因果关系，例如阻断离子通道或突触连接对神经元活动的影响。由于开发一个生物病理学上真实的，单神经元模型是非常困难的，软件已经开发自动调整计算神经元模型的参数。理想的优化软件应该与常用的神经模拟软件一起工作;因此，我们提出了与MOOSE模拟器的声明格式指定的模型一起工作的软件。可以使用两种不同的文件格式之一来指定实验数据。适应度函数可定制为特征差异的加权组合。优化本身使用协方差矩阵自适应进化策略，因为它在面对适应度函数的局部波动时具有鲁棒性，并且可以很好地处理高维和不连续的适应度景观。我们证明了该软件的多功能性，通过创建四种类型的神经元（两种亚型的棘状投射神经元和两种亚型的苍白球神经元）的几个模型的例子，通过调整电流钳数据。优化在1，600 - 4，000次模型评估（200-500代×种群大小为8）内达到收敛。最佳拟合模型的参数分析揭示了神经元亚型之间的差异，这与先前的实验结果一致。总的来说，我们的研究结果表明，这种易于使用的，自动的方法来寻找神经元通道参数可以应用于电流钳记录从神经元表现出不同的生化标志物，以帮助表征其他神经元亚型之间的离子差异。
Computational models in neuroscience can be used to predict causal relationships between biological mechanisms in neurons and networks, such as the effect of blocking an ion channel or synaptic connection on neuron activity. Since developing a biophysically realistic, single neuron model is exceedingly difficult, software has been developed for automatically adjusting parameters of computational neuronal models. The ideal optimization software should work with commonly used neural simulation software; thus, we present software which works with models specified in declarative format for the MOOSE simulator. Experimental data can be specified using one of two different file formats. The fitness function is customizable as a weighted combination of feature differences. The optimization itself uses the covariance matrix adaptation-evolutionary strategy, because it is robust in the face of local fluctuations of the fitness function, and deals well with a high-dimensional and discontinuous fitness landscape. We demonstrate the versatility of the software by creating several model examples of each of four types of neurons (two subtypes of spiny projection neurons and two subtypes of globus pallidus neurons) by tuning to current clamp data. Optimizations reached convergence within 1,600–4,000 model evaluations (200–500 generations × population size of 8). Analysis of the parameters of the best fitting models revealed differences between neuron subtypes, which are consistent with prior experimental results. Overall our results suggest that this easy-to-use, automatic approach for finding neuron channel parameters may be applied to current clamp recordings from neurons exhibiting different biochemical markers to help characterize ionic differences between other neuron subtypes.
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小鼠外部苍白球中的乙醇敏感起搏器神经元。
DOI：
10.1038/npp.2016.251
发表时间：
2017-04
期刊：
Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
影响因子：
--
作者：
Abrahao KP;Chancey JH;Chan CS;Lovinger DM
通讯作者：
Lovinger DM
影响因子：
5.3
作者：
Damodaran, Sriraman;Cressman, John R.;Blackwell, Kim T.
通讯作者：
Blackwell, Kim T.
影响因子：
2.5
作者：
Brookings, Ted;Goeritz, Marie L.;Marder, Eve
通讯作者：
Marder, Eve
影响因子：
3.5
作者：
Friedrich P;Vella M;Gulyás AI;Freund TF;Káli S
通讯作者：
Káli S
影响因子：
4.3
作者：
Druckmann, Shaul;Banitt, Yoav;Gidon, Albert;Schurmann, Felix;Markram, Henry;Segev, Idan
通讯作者：
Segev, Idan