Biologically relevant large-scale computational models currently represent one of the main methods in neuroscience for studying information processing primitives of brain areas. However, biologically realistic neuron models tend to be computationally heavy and thus prevent these models from being part of brain-area models including thousands or even millions of neurons. The cerebellar input layer represents a canonical example of large scale networks. In particular, the cerebellar granule cells, the most numerous cells in the whole mammalian brain, have been proposed as playing a pivotal role in the creation of somato-sensorial information representations. Enhanced burst frequency (spiking resonance) in the granule cells has been proposed as facilitating the input signal transmission at the theta-frequency band (4–12 Hz), but the functional role of this cell feature in the operation of the granular layer remains largely unclear. This study aims to develop a methodological pipeline for creating neuron models that maintain biological realism and computational efficiency whilst capturing essential aspects of single-neuron processing. Therefore, we selected a light computational neuron model template (the adaptive-exponential integrate-and-fire model), whose parameters were progressively refined using an automatic parameter tuning with evolutionary algorithms (EAs). The resulting point-neuron models are suitable for reproducing the main firing properties of a realistic granule cell from electrophysiological measurements, including the spiking resonance at the theta-frequency band, repetitive firing according to a specified intensity-frequency (I-F) curve and delayed firing under current-pulse stimulation. Interestingly, the proposed model also reproduced some other emergent properties (namely, silent at rest, rheobase and negligible adaptation under depolarizing currents) even though these properties were not set in the EA as a target in the fitness function (FF), proving that these features are compatible even in computationally simple models. The proposed methodology represents a valuable tool for adjusting AdEx models according to a FF defined in the spiking regime and based on biological data. These models are appropriate for future research of the functional implication of bursting resonance at the theta band in large-scale granular layer network models.

目前，与生物有关的大规模计算模型代表了神经科学中研究大脑区域信息处理原语的主要方法之一。但是，生物学上逼真的神经元模型往往计算量大，因此阻止了这些模型成为包括数千甚至数百万个神经元的大脑区域模型的一部分。小脑输入层代表大规模网络的典型示例。特别地，已经提出小脑颗粒细胞是整个哺乳动物脑中数量最多的细胞，在创建体感信息表示中起着关键作用。颗粒细胞中增强的猝发频率（尖峰共振）已被提出，以促进在theta频带（4–12 Hz）的输入信号传输，但是该细胞特征在颗粒层操作中的功能作用仍然不清楚。这项研究旨在开发一种方法流程，以创建神经元模型，从而在保留单神经元处理的重要方面的同时，保持生物现实性和计算效率。因此，我们选择了一种轻型计算神经元模型模板（自适应指数积分和射击模型），该模板的参数通过使用带有进化算法（EA）的自动参数调整来逐步完善。所得的点神经元模型适用于根据电生理学测量（包括在theta频段的尖峰共振）重现真实颗粒细胞的主要发射特性，根据指定的强度-频率（IF）曲线重复发射，并在电流脉冲刺激下延迟发射。有趣的是，即使在EA中未将这些特性设置为适应度函数（FF）的目标，所提出的模型也重现了其他一些新兴特性（即，静止无声，流变碱和在去极化电流下的适应性可忽略不计），证明了这些特性即使在计算简单的模型中，功能也兼容。所提出的方法学是根据加标机制中定义的FF并根据生物学数据调整AdEx模型的有价值的工具。这些模型适用于将来在大规模颗粒层网络模型中theta波段的爆裂共振的功能含义的研究。即使在EA中未将这些特性设置为适应度函数（FF）的目标，所提出的模型也重现了其他一些新兴特性（即，静默，流变基和去极化电流下可忽略的适应性），证明了这些特性是即使在计算简单的模型中也兼容。所提出的方法学是根据加标机制中定义的FF并根据生物学数据调整AdEx模型的有价值的工具。这些模型适用于将来在大规模颗粒层网络模型中theta波段的爆裂共振的功能含义的研究。即使在EA中未将这些特性设置为适应度函数（FF）的目标，所提出的模型也重现了其他一些新兴特性（即，静默，流变基和去极化电流下可忽略的适应性），证明了这些特性是即使在计算简单的模型中也兼容。所提出的方法学是根据加标机制中定义的FF并根据生物学数据调整AdEx模型的有价值的工具。这些模型适用于将来在大规模颗粒层网络模型中theta波段的爆裂共振的功能含义的研究。即使在EA中没有将这些属性设置为适应度函数（FF）的目标，在去极化电流下的流变碱和微不足道的适应性也是如此，这证明这些功能即使在计算简单的模型中也兼容。所提出的方法学是根据加标机制中定义的FF并根据生物学数据调整AdEx模型的有价值的工具。这些模型适用于将来在大规模颗粒层网络模型中theta波段上爆发共振的功能含义的研究。即使在EA中未将这些属性设置为适应度函数（FF）的目标，在去极化电流下的流变碱和微不足道的适应性也是如此，这证明这些功能即使在计算简单的模型中也兼容。所提出的方法学是根据加标机制中定义的FF并根据生物学数据调整AdEx模型的有价值的工具。这些模型适用于将来在大规模颗粒层网络模型中theta波段的爆裂共振的功能含义的研究。