Marín Milagros, Cruz Nicolás C, Ortigosa Eva M, Sáez-Lara María J, Garrido Jesús A, Carrillo Richard R
Department of Biochemistry and Molecular Biology I, University of Granada, Granada, Spain.
Department of Informatics, University of Almería, ceiA3, Almería, Spain.
Front Neuroinform. 2021 Jun 3;15:663797. doi: 10.3389/fninf.2021.663797. eCollection 2021.
This article extends a recent methodological workflow for creating realistic and computationally efficient neuron models whilst capturing essential aspects of single-neuron dynamics. We overcome the intrinsic limitations of the extant optimization methods by proposing an alternative optimization component based on multimodal algorithms. This approach can natively explore a diverse population of neuron model configurations. In contrast to methods that focus on a single global optimum, the multimodal method allows directly obtaining a set of promising solutions for a single but complex multi-feature objective function. The final sparse population of candidate solutions has to be analyzed and evaluated according to the biological plausibility and their objective to the target features by the expert. In order to illustrate the value of this approach, we base our proposal on the optimization of cerebellar granule cell (GrC) models that replicate the essential properties of the biological cell. Our results show the emerging variability of plausible sets of values that this type of neuron can adopt underlying complex spiking characteristics. Also, the set of selected cerebellar GrC models captured spiking dynamics closer to the reference model than the single model obtained with off-the-shelf parameter optimization algorithms used in our previous article. The method hereby proposed represents a valuable strategy for adjusting a varied population of realistic and simplified neuron models. It can be applied to other kinds of neuron models and biological contexts.
本文扩展了一种最近的方法工作流程，用于创建逼真且计算高效的神经元模型，同时捕捉单神经元动力学的基本方面。我们通过提出一种基于多模态算法的替代优化组件，克服了现有优化方法的内在局限性。这种方法可以自然地探索各种神经元模型配置。与专注于单个全局最优解的方法不同，多模态方法允许直接为单个但复杂的多特征目标函数获得一组有前景的解决方案。最终的候选解决方案稀疏群体必须由专家根据生物学合理性及其与目标特征的契合度进行分析和评估。为了说明这种方法的价值，我们将提议基于对复制生物细胞基本特性的小脑颗粒细胞（GrC）模型的优化。我们的结果显示了这种类型的神经元在潜在复杂放电特征下可以采用的合理值集的新出现的变异性。此外，与我们上一篇文章中使用现成参数优化算法获得的单个模型相比，所选的小脑GrC模型集捕捉到的放电动力学更接近参考模型。本文提出的方法是调整各种逼真且简化的神经元模型群体的一种有价值的策略。它可以应用于其他类型的神经元模型和生物学背景。
