This work compares different algorithms to replace the genetic optimizer used in a recent methodology for creating realistic and computationally efficient neuron models. That method focuses on single-neuron processing and has been applied to cerebellar granule cells. It relies on the adaptive-exponential integrate-and-fire (AdEx) model, which must be adjusted with experimental data. The alternatives considered are: i) a memetic extension of the original genetic method, ii) Differential Evolution, iii) Teaching-Learning-Based Optimization, and iv) a local optimizer within a multi-start procedure. All of them ultimately outperform the original method, and the last two do it in all the scenarios considered. PDF  XML

中文翻译：

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拟合神经元模型的随机优化器的比较研究。在小脑颗粒细胞中的应用

这项工作比较了不同的算法，以取代最近的方法中使用的遗传优化器，以创建逼真的，计算效率高的神经元模型。该方法专注于单神经元处理，并已应用于小脑颗粒细胞。它依赖于自适应指数积分和发射（AdEx）模型，该模型必须根据实验数据进行调整。考虑的替代方案包括：i）原始遗传方法的模因扩展； ii）差异进化； iii）基于教与学的优化；以及iv）多启动过程中的本地优化器。它们最终都胜过了原始方法，而后两个方法在所有考虑的场景中都做到了。PDF XML